Method for determining a fingerprint motion state, navigation control method, and electronic device

Abstract

The application discloses a method for determining a fingerprint motion state, comprising: acquiring a to-be-processed fingerprint image collected by a fingerprint sensor corresponding to an i-th control period; processing the to-be-processed fingerprint image according to a target algorithm at an execution time point of the target algorithm in the i-th control period to obtain a target algorithm processing result in the i-th control period; the target algorithm processing result comprises a target similarity and a target centroid offset, the target centroid offset comprises a target centroid offset distance and direction information, the target centroid offset distance is used for representing a sliding distance of a finger on a fingerprint collection area, and the direction information is used for representing a sliding direction; performing confidence classification on the target similarity and the target centroid offset to obtain a confidence classification result belonging to the target similarity and the target centroid offset; and determining an i-th motion state of the finger at the execution time point of the target algorithm in the i-th control period according to the confidence classification result belonging to the target similarity and the target centroid offset.

Description

Technical Field

[0001] This application relates to the field of electronic equipment technology, and in particular to a method for determining the motion state of a fingerprint, a navigation control method, and an electronic device. Background Technology

[0002] With the continuous development of fingerprint recognition technology and the upgrading of electronic device hardware configurations, fingerprint sensors are widely used in electronic devices, providing reliable protection for the information security of these devices. Meanwhile, with the continuous development of human-computer interaction technology, in addition to providing authentication functions, fingerprint sensors also allow users to control electronic devices by sliding their fingers up and down on the fingerprint collection area. This includes functions such as volume control, interface scrolling, or progress bars, providing users with a new way to interact with electronic devices.

[0003] According to the relevant technology, when a user's finger is detected to be effectively touching the fingerprint collection area, the fingerprint sensor of the electronic device will capture an image of the finger. The processor of the electronic device will then perform algorithmic processing on this fingerprint image and the previously captured fingerprint image. After completing the algorithmic processing, the processor will determine the movement state of the finger in the fingerprint collection area based on the algorithmic processing result, and perform navigation control on the controlled program of the electronic device based on this movement state. Subsequently, the fingerprint sensor will capture another image of the finger, and the processor will perform algorithmic processing on this fingerprint image and the previously captured fingerprint image. Based on the algorithmic processing result, the processor will determine the movement state of the finger in the fingerprint collection area again, and perform navigation control on the controlled program of the electronic device based on this movement state. This process of image acquisition, algorithmic processing, and navigation control is repeated until the user's finger is detected to have left the fingerprint collection area.

[0004] However, the fingerprint processing algorithms in related technologies are inaccurate in judging the movement state of the finger when processing the fingerprint image collected by the fingerprint sensor, which causes the navigation control of the controlled program of the electronic device to not meet the user's expectations. Summary of the Invention

[0005] This application provides a method for determining the movement state of a fingerprint, a navigation control method, and an electronic device, which can accurately determine the movement state of a finger, thereby enabling the controlled program of the electronic device to perform navigation control in accordance with the user's expectations.

[0006] According to a first aspect of this application, a method for determining the motion state of a fingerprint is disclosed, applied to an electronic device, the electronic device including a fingerprint sensor, the method comprising:

[0007] Obtain the fingerprint image to be processed collected by the fingerprint sensor corresponding to the i-th control cycle; where i≥1;

[0008] At the execution time point of the target algorithm within the i-th control cycle, the fingerprint image to be processed is processed according to the target algorithm to obtain the target algorithm processing result within the i-th control cycle; wherein, the target algorithm processing result includes: target similarity and target centroid offset, the target centroid offset includes: target centroid offset distance and direction information, the target centroid offset distance is used to characterize the sliding distance of the finger on the fingerprint acquisition area, and the direction information is used to characterize the sliding direction of the finger on the fingerprint acquisition area;

[0009] The target similarity and the target centroid offset are classified with confidence to obtain the confidence classification results to which the target similarity and the target centroid offset belong;

[0010] Based on the target similarity and the confidence classification result of the target centroid offset, the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle is determined.

[0011] According to a second aspect of this application, a navigation control method is disclosed, applied to an electronic device, the electronic device including a fingerprint sensor, the method comprising:

[0012] Based on the method for determining the fingerprint motion state in the first aspect, the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle is determined;

[0013] At each candidate image acquisition time point within the i-th control cycle, an i-th navigation control signal is generated for the electronic device based on the i-th motion state.

[0014] According to a third aspect of this application, an electronic device is disclosed, comprising: a fingerprint sensor, a memory, a processor, and a computer program stored in the memory, wherein the fingerprint sensor is used to acquire fingerprint images, and the processor executes the computer program to implement the method for determining fingerprint motion state as in the first aspect, or to implement the navigation control method as in the second aspect.

[0015] According to a fourth aspect of this application, a computer-readable storage medium is disclosed having a computer program / instructions stored thereon, which, when executed by a processor, implements the method for determining fingerprint motion state as in the first aspect, or the navigation control method as in the second aspect.

[0016] According to a fifth aspect of this application, a computer program product is disclosed, comprising a computer program / instructions that, when executed by a processor, implement the method for determining fingerprint motion state as in the first aspect, or the navigation control method as in the second aspect.

[0017] In this embodiment, the fingerprint image to be processed is acquired by the fingerprint sensor corresponding to the i-th control cycle; where i ≥ 1; at the execution time point of the target algorithm within the i-th control cycle, the fingerprint image to be processed is processed according to the target algorithm to obtain the target algorithm processing result within the i-th control cycle; wherein, the target algorithm processing result includes: target similarity and target centroid offset, the target centroid offset includes: target centroid offset distance and direction information, the target centroid offset distance is used to characterize the sliding distance of the finger on the fingerprint acquisition area, and the direction information is used to characterize the sliding direction of the finger on the fingerprint acquisition area; since the similarity and centroid offset between two fingerprint images can reflect the motion state of the user's finger on the fingerprint acquisition area, and confidence is an indicator for measuring the reliability of the prediction result, the target similarity and target centroid offset are classified by confidence. According to the confidence classification result to which the target similarity and target centroid offset belong, the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle can be accurately determined, thereby realizing navigation control of the controlled program of the electronic device in accordance with the user's expectations. Attached Figure Description

[0018] Figure 1 This is one of the example images of a finger sliding on the fingerprint collection area provided in the embodiments of this application;

[0019] Figure 2 This is the second example diagram of a finger sliding on the fingerprint collection area provided in the embodiments of this application;

[0020] Figure 3 This is the third example of a finger sliding on the fingerprint collection area provided in the embodiments of this application;

[0021] Figure 4A This is one of the example diagrams of navigation control flow in related technologies;

[0022] Figure 4B This is the second example diagram of the navigation control process in related technologies;

[0023] Figure 5 This is a flowchart of a method for determining fingerprint motion state provided in an embodiment of this application;

[0024] Figure 6 This is an example image of a fingerprint image provided in an embodiment of this application;

[0025] Figure 7 This is one of the example diagrams illustrating a control cycle division method provided in the embodiments of this application;

[0026] Figure 8 This is a second example diagram of a control cycle division method provided in the embodiments of this application;

[0027] Figure 9 This is a flowchart of a navigation control method provided in an embodiment of this application;

[0028] Figure 10 This is a schematic diagram of the structure of a device for determining the movement state of a fingerprint, provided in an embodiment of this application;

[0029] Figure 11 This is a schematic diagram of the structure of a navigation control device provided in an embodiment of this application;

[0030] Figure 12 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0033] To facilitate understanding, the following section first introduces some relevant concepts and application scenarios involved in the embodiments of this application.

[0034] I. Related Concepts

[0035] A fingerprint sensor, also known as a fingerprint sensor, is a sensing device that can be optical, capacitive, or ultrasonic fingerprint sensors, and is a key component for automatic fingerprint acquisition. Currently, fingerprint sensors are increasingly widely used in mobile devices for unlocking, payments, and other applications.

[0036] A side-mounted fingerprint sensor is a design that places the fingerprint sensor on the side of an electronic device, typically at its edge. This design not only solves the problem of natural finger placement but also makes unlocking smoother. Users can easily unlock the device simply by holding it, without changing their grip. Currently, side-mounted fingerprint sensors primarily utilize capacitive fingerprint recognition technology. This technology identifies fingerprints by measuring the capacitance change between the fingerprint dent and the fingerprint pattern. Capacitive side-mounted fingerprint sensors can sense the texture of the fingerprint, offering a high level of security. However, side-mounted fingerprint sensors can also be other types of fingerprint sensors, such as ultrasonic fingerprint sensors.

[0037] The fingerprint collection area can be located on the fingerprint sensor, such as a capacitive fingerprint sensor; or it can be on the display screen, in which case the fingerprint sensor is usually located under the screen, such as an optical fingerprint sensor or an ultrasonic fingerprint sensor. For a fingerprint collection area located on the fingerprint sensor, such as a capacitive fingerprint sensor, touching the fingerprint collection area means that the finger directly touches the capacitive fingerprint sensor; for an under-display fingerprint sensor, such as an optical fingerprint sensor or an ultrasonic fingerprint sensor, touching the fingerprint collection area means that the finger touches the screen above the under-display fingerprint sensor.

[0038] For electronic devices equipped with side fingerprint sensors, since most side fingerprint sensors are elongated (longer in length and shorter in width), their fingerprint collection area is usually narrow and elongated. The sliding of the finger on the fingerprint collection area mainly includes the following three types: 1) ... Figure 1 As shown, the extension direction of finger 12 is parallel to the long side of the fingerprint collection area 11 of electronic device 10, and slides up and down along the long side; this sliding is called "vertical swipe"; 2) as Figure 2 As shown, the extension direction of finger 22 is parallel to the long side of the fingerprint collection area 21 of electronic device 20, and slides left and right along the short side of the fingerprint collection area 21. This sliding is called "lateral sliding"; 3) as Figure 3 As shown, the extension direction of the finger 32 is perpendicular to the long side of the fingerprint collection area 31 of the electronic device 30, and slides left and right along the short side of the fingerprint collection area 31. This sliding is called "vertical sliding".

[0039] Considering that most side-mounted fingerprint sensors are elongated, meaning they are longer and narrower, their fingerprint collection area is typically narrow and elongated. Users are more accustomed to sliding their fingers along the length of this area, i.e., a "vertical swipe." Therefore, for electronic devices equipped with side-mounted fingerprint sensors, this application embodiment primarily uses the vertical swipe scenario of the finger on its fingerprint collection area as an example for illustration. However, the method for determining the fingerprint movement state and the navigation control method in this application embodiment are also applicable to the other two swiping scenarios. Furthermore, while a side-mounted fingerprint sensor is preferred, this is only an example and not a limitation. This application embodiment can be applied to front and rear fingerprint sensors of electronic devices, and the type can be an optical fingerprint sensor, a capacitive fingerprint sensor, an ultrasonic fingerprint sensor, etc.

[0040] II. Application Scenarios

[0041] With the continuous development of fingerprint recognition technology and the upgrading of electronic device hardware configurations, fingerprint recognition technology based on small-area side fingerprint sensors has been widely used in electronic devices, providing reliable protection for the information security of electronic devices. Taking side fingerprint sensors as an example, currently, since most side fingerprint sensors are elongated in shape, they are well-suited for sliding fingers up and down on their fingerprint collection area. Therefore, users can control the electronic device's programs by sliding their fingers up and down on the fingerprint collection area, such as volume control, interface scroll bars, or progress bars, providing users with a new way to interact with electronic devices.

[0042] Navigation control processes in related technologies, such as Figure 4A As shown, this involves the hardware and software layers of an electronic device. The hardware layer refers to the specific physical sensor layer of the electronic device, such as a side-mounted fingerprint sensor. The software layer, running on the processor of the electronic device, is the layer that can directly manipulate the physical sensors of the hardware layer. For example, the software layer can control the side-mounted fingerprint sensor to acquire fingerprint images and perform algorithmic processing on the fingerprint images acquired by the side-mounted fingerprint sensor. The software layer may include: a software algorithm layer, a software control layer, and a controlled program layer, etc.

[0043] When a user's finger is detected to be effectively touching the fingerprint acquisition area, the hardware and software layers of the electronic device interact. This interaction process generally includes the following steps: Step 401, the side fingerprint sensor acquires a fingerprint image and reports it to the software algorithm layer; Step 402, the software algorithm layer's program processes the currently reported fingerprint image and the previously reported fingerprint image from the side fingerprint sensor using an algorithm to obtain the algorithm result; Step 403, after completing the algorithm processing, the software algorithm layer's program controls the side fingerprint sensor to acquire the next fingerprint image; Step 404, after completing the algorithm processing, the software... The algorithm layer program determines the finger's movement state based on the algorithm result and reports it to the software control layer; in step 405, the software control layer program sends a navigation control signal to the controlled program based on the finger's movement state. For example, when the controlled program is a volume adjustment program, if the finger's movement state is sliding downwards, the navigation control signal is a signal used to control the volume adjustment program to lower the volume; in step 406, the controlled program performs a corresponding operation based on the navigation control signal. For example, the navigation control signal is a signal used to control the volume adjustment program to lower the volume, and the volume adjustment program lowers the volume of the electronic device based on the navigation control signal.

[0044] For example, such as Figure 4B As shown, in the scenario where the side fingerprint sensor in the hardware layer acquires an image for the first time (i.e., acquires a fingerprint image), since there is no previous fingerprint image, the side fingerprint sensor acquires images twice consecutively. The acquired first and second fingerprint images are reported to the software algorithm layer. The program in the software algorithm layer performs the first algorithm processing on these two fingerprint images to obtain the algorithm result. Based on the algorithm result, the movement state of the finger is determined, and the movement state of the finger is reported to the software control layer for the first time. This allows the program in the software control layer to send navigation control signals to the controlled program based on the movement state of the finger, thereby achieving one-time control of the controlled program.

[0045] After completing the first algorithm processing, the software algorithm layer controls the side fingerprint sensor to perform a third image acquisition. The side fingerprint sensor then reports the third fingerprint image to the software algorithm layer. The software algorithm layer then performs a second algorithm processing on the third fingerprint image reported by the fingerprint sensor and the previously reported second fingerprint image to obtain the algorithm result. Based on the algorithm result, the finger's movement state is determined, and the finger's movement state is reported to the software control layer for the second time. This allows the software control layer to send navigation control signals to the controlled program based on the finger's movement state, thereby achieving further control over the controlled program. This process continues until the side fingerprint sensor performs a fourth image acquisition, and so on, until the finger is detected to have left the fingerprint acquisition area.

[0046] However, fingerprint processing algorithms in related technologies often fail to accurately determine the movement state of the finger when processing fingerprint images captured by fingerprint sensors, resulting in navigation control of the controlled program of the electronic device not meeting user expectations. To address these technical problems, embodiments of this application provide a method for determining fingerprint movement state, a navigation control method, and an electronic device.

[0047] The methods for determining fingerprint motion state and navigation control methods provided in the embodiments of this application will be described below with reference to the accompanying drawings.

[0048] It should be noted that the methods for determining fingerprint motion state and navigation control provided in this application are applicable to electronic devices. In practical applications, the electronic device may include mobile terminals such as smartphones, tablets, personal digital assistants, and attendance machines. This application does not limit this.

[0049] Figure 5 This is a flowchart illustrating a method for determining fingerprint motion state according to an embodiment of this application, applied to an electronic device, which includes a fingerprint sensor, such as... Figure 5 As shown, the method may include the following steps: step 501, step 502, step 503 and step 504;

[0050] In step 501, the fingerprint image to be processed collected by the fingerprint sensor corresponding to the i-th control cycle is obtained; where i≥1.

[0051] In this embodiment of the application, when a finger is detected to be effectively touching the fingerprint collection area, the fingerprint image to be processed collected by the fingerprint sensor in the i-th control cycle can be obtained. Here, effective touch refers to touch that meets the contact requirements. For example, the contact requirements may include reaching the target pressing pressure and / or the target pressing area, so as to determine that the touch is not a mistaken touch.

[0052] In this embodiment of the application, since the size and position of the fingerprint collection area on the electronic device are fixed, the processor of the electronic device can determine whether the finger touches the fingerprint collection area based on the touch coordinates of the finger, wherein the touch coordinates are used to represent the touch position of the finger on the electronic device.

[0053] In some embodiments, the fingerprint sensor described above may be located at one of the following locations: the side of the electronic device, the front of the electronic device, or the back of the electronic device.

[0054] In this embodiment, when the fingerprint sensor is located on the side of the electronic device, it is a side-mounted fingerprint sensor. When the fingerprint sensor is located on the front of the electronic device, it is a front-mounted fingerprint sensor, such as an under-display fingerprint sensor. When the fingerprint sensor is located on the back of the electronic device, it is a rear-mounted fingerprint sensor.

[0055] As can be seen, in this embodiment of the application, fingerprint sensors located at different positions on the electronic device can all achieve navigation control of the controlled program of the electronic device, which is highly flexible and applicable to a wide range of scenarios.

[0056] In this embodiment, since the software layer runs on the processor of the electronic device and the hardware layer is mainly the fingerprint sensor, the description is based on the processor of the electronic device as the execution subject when the controlled program of the electronic device is navigated based on the user's finger sliding up and down on the fingerprint collection area.

[0057] In this embodiment of the application, the number of fingerprint images to be processed collected by the fingerprint sensor in each control cycle is at least two.

[0058] In some embodiments, considering that the fingerprint sensor is composed of a sensor pixel array, and each sensor pixel in the sensor pixel array has a photosensitive element to realize the conversion of light signals to electrical signals, in order to shorten the image acquisition interval, the fingerprint sensor can be configured to perform fingerprint acquisition in alternate rows and columns. Accordingly, the provided navigation control method may also include the following steps before the above step 501: Step 500.

[0059] In step 500, personalized image acquisition parameters of the fingerprint sensor are configured; wherein, the personalized image acquisition parameters include at least one of the following: N and M, where N is the number of sensor pixel rows between two adjacent image pixel rows used to acquire fingerprint images in the fingerprint sensor, i.e., the row acquisition step size of the fingerprint sensor; M is the number of sensor pixel columns between two adjacent image pixel columns used to acquire fingerprint images in the fingerprint sensor, i.e., the column acquisition step size of the fingerprint sensor; N≥1, M≥1.

[0060] In this embodiment of the application, the sensor pixel rows and columns with the above-mentioned intervals are not used for fingerprint image acquisition.

[0061] In this embodiment of the application, for a fingerprint sensor that supports interlaced image acquisition, the row acquisition step size N and column acquisition step size M of the fingerprint sensor can be configured according to the processor's algorithm processing capability. The stronger the processor's algorithm processing capability, the larger the values ​​of N and M can be.

[0062] For example, the fingerprint sensor's pixel array has 80 rows and 20 columns. When all rows and columns of the fingerprint sensor are captured, the size of the captured fingerprint image is 80*20 pixels. When N is configured to 1 and M is configured to 1, the size of the fingerprint image captured by the fingerprint sensor is 40*10 pixels. Compared to the full-size fingerprint image, the size of the fingerprint image captured by alternating rows and columns is reduced by 4 times, and the image acquisition time is also reduced accordingly.

[0063] In this embodiment of the application, for fingerprint sensors that support interlaced image acquisition, the fingerprint sensor can be controlled to acquire fingerprints of the user's finger in interlaced rows and columns by pre-configuring personalized image acquisition parameters. Compared with fingerprint acquisition of all sensor pixel rows and columns, interlaced fingerprint acquisition can shorten the time of each acquisition, thereby reducing the acquisition interval between two adjacent fingerprint images.

[0064] In step 502, at the execution time of the target algorithm within the i-th control cycle, the fingerprint image to be processed is processed according to the target algorithm to obtain the target algorithm processing result within the i-th control cycle; wherein, the target algorithm processing result includes: target similarity and target centroid offset, the target centroid offset includes: target centroid offset distance and direction information, the target centroid offset distance is used to characterize the sliding distance of the finger on the fingerprint acquisition area, and the direction information is used to characterize the sliding direction of the finger on the fingerprint acquisition area.

[0065] In this embodiment, the target algorithm includes a fingerprint comparison algorithm and a centroid offset calculation algorithm. The fingerprint comparison algorithm calculates the similarity between two fingerprint images. If the similarity is high, it indicates that the user's finger is likely stationary or undergoing minor rubbing or shaking; if the similarity is low, it indicates that the user's finger is likely sliding. The centroid offset calculation algorithm calculates the centroid offset between two fingerprint images. If the centroid offset is small, it indicates that the user's finger is likely stationary or undergoing minor rubbing or shaking; if the centroid offset is large, it indicates that the user's finger is likely sliding.

[0066] In this embodiment, on one hand, the distance portion of the centroid offset reflects the straight-line distance between the centroids of two fingerprints (i.e., the centroids of two fingerprint images). When the centroids of the two fingerprints move, the distance portion of the centroid offset changes. This change can be used to quantify the distance the fingerprint moves in space, thereby revealing its motion state. On the other hand, the direction portion of the centroid offset indicates the vector direction from one fingerprint centroid to another. This directional information is crucial for understanding the movement trajectory of the finger. By analyzing the direction of the centroid offset, it can be determined whether the finger moves left, right, up, down, or along any other arbitrary direction. This directional information is very useful for tracking and predicting fingerprint motion patterns. Therefore, combining the above two dimensions, the centroid offset provides a comprehensive and accurate description for quantifying and analyzing the motion state or changes between two fingerprints.

[0067] In some embodiments, step 502 may include the following steps: step 5021, step 5022, step 5023, step 5024 and step 5025;

[0068] In step 5021, when the fingerprint sensor is configured with personalized image acquisition parameters, the fingerprint images to be processed are all interpolated to obtain full-size fingerprint images; where full size is the size of the fingerprint image acquired when all sensor pixels of the fingerprint sensor are used for fingerprint image acquisition.

[0069] In this embodiment of the application, when the fingerprint sensor is configured with personalized image acquisition parameters, since each fingerprint image acquired by the fingerprint sensor is not a full-size fingerprint image, in order to ensure the accuracy of the algorithm processing results, it is first necessary to perform interpolation processing on all fingerprint images to be processed to obtain a full-size fingerprint image.

[0070] It should be noted that if the fingerprint sensor is not configured with personalized image acquisition parameters and uses all sensor pixels for image acquisition, step 5021 above can be omitted.

[0071] In step 5022, the full-size fingerprint image is subjected to shading processing to obtain the shading-removed fingerprint image.

[0072] In this embodiment of the application, in order to improve the quality of fingerprint images, remove noise, enhance feature points, and ultimately improve the accuracy and efficiency of fingerprint recognition, the fingerprint images are subjected to stencil removal processing.

[0073] In this embodiment of the application, the background removal process includes, but is not limited to: image segmentation processing, noise removal processing, and fingerprint feature enhancement processing.

[0074] In step 5023, fingerprint features are extracted from the fingerprint image after removing the ridges to obtain at least two sets of fingerprint features; wherein, one fingerprint image corresponds to one set of fingerprint features.

[0075] In this embodiment of the application, fingerprint features may include global features and local features. Global features include, but are not limited to, fingerprint type (whorl, loop, etc.), and the positions of the center point and triangular points. Local features include, but are not limited to, the starting or ending points of fingerprint ridges, and the intersection points formed by the intersection of two or more ridges.

[0076] In this application embodiment, the fingerprint feature extraction method includes, but is not limited to, template matching method and mathematical morphology method.

[0077] In step 5024, at least two sets of fingerprint features are aligned to obtain at least two aligned sets of fingerprint features.

[0078] In this embodiment of the application, fingerprint features are aligned to ensure the accuracy of fingerprint comparison results and centroid offset calculation results.

[0079] In this application, the fingerprint alignment method includes, but is not limited to: a reference point-based method, a method based on additional information, a method based on image correlation, and a method based on feature descriptors.

[0080] In step 5025, the target similarity and target centroid offset are obtained based on at least two aligned sets of fingerprint features.

[0081] In this embodiment of the application, for two sets of fingerprint features, a fingerprint comparison algorithm is used to calculate a similarity; for two sets of fingerprint features, a centroid offset calculation algorithm is used to calculate a centroid offset.

[0082] Furthermore, step 5025 above may include the following steps: step 50251, step 50252 and step 50253;

[0083] In step 50251, at least one set of candidate similarity and candidate centroid offset are calculated based on each pair of aligned fingerprint features.

[0084] In this embodiment of the application, a set of candidate similarities and candidate centroid offsets includes: a candidate similarity and a candidate centroid offset.

[0085] In step 50252, when the number of fingerprint images to be processed is equal to two, the calculated set of candidate similarity and candidate centroid offset are determined as target similarity and target centroid offset, respectively.

[0086] In step 50253, if the number of fingerprint images to be processed is greater than two, the largest one is selected from at least one set of candidate similarity and candidate centroid offset as the target similarity and target centroid offset, respectively.

[0087] In this embodiment of the application, for a fingerprint sensor configured with personalized image acquisition parameters, multiple image processing steps can be performed on the fingerprint image to be processed acquired by it, thereby ensuring the accuracy of the target algorithm processing result.

[0088] In some embodiments, when the fingerprint sensor is not configured with personalized image acquisition parameters, since each fingerprint image acquired by the fingerprint sensor is a full-size fingerprint image, the full-size fingerprint image can be directly de-textured to obtain a de-textured fingerprint image; fingerprint features are extracted from the de-textured fingerprint image to obtain at least two sets of fingerprint features; wherein, one fingerprint image corresponds to one set of fingerprint features; the at least two sets of fingerprint features are aligned to obtain at least two aligned sets of fingerprint features; based on each pair of aligned fingerprint features, at least one set of candidate similarity and candidate centroid offset are calculated respectively; when the number of fingerprint images to be processed is equal to two, the calculated set of candidate similarity and candidate centroid offset are determined as the target similarity and target centroid offset respectively; when the number of fingerprint images to be processed is greater than two, the largest of the at least one set of candidate similarity and candidate centroid offset is selected as the target similarity and target centroid offset respectively.

[0089] In step 503, the target similarity and target centroid offset are classified with confidence to obtain the confidence classification results of the target similarity and target centroid offset.

[0090] In this embodiment of the application, considering that confidence is an indicator for measuring the reliability of prediction results, the confidence of target similarity and target centroid offset can be classified, and the movement state of the finger can be determined according to the confidence classification results of target similarity and target centroid offset.

[0091] In some embodiments, step 503 above may include the following step: step 5031;

[0092] In step 5031, the target similarity and target centroid offset are classified with confidence based on the similarity threshold set and the offset distance threshold set to obtain the confidence classification results of the target similarity and target centroid offset; wherein, the similarity threshold set includes at least one threshold, and the threshold in the similarity threshold set is used to classify the target similarity with confidence; the offset distance threshold set includes at least one threshold, and the threshold in the offset distance threshold set is used to classify the target centroid offset with confidence; the confidence classification results of the target similarity and the confidence classification results of the target centroid offset are used to determine the i-th motion state.

[0093] In this embodiment of the application, in order to identify the directional sliding of a finger on the fingerprint collection area, a similarity threshold set and an offset distance threshold set can be set. By comparing the target similarity with the threshold in the similarity threshold set and comparing the target centroid offset with the threshold in the offset distance threshold set, the sliding direction of the directional sliding can be accurately determined.

[0094] In the embodiments of this application, the directional sliding mainly includes the following four types: changing from stationary to upward sliding, changing from stationary to downward sliding, changing from upward sliding to downward sliding, and changing from downward sliding to upward sliding.

[0095] In some embodiments, the user's swiping behavior may exhibit an inertial retracement pattern, where the user swipes up before swiping down and vice versa. For example... Figure 6 The seven adjacent fingerprint images shown indicate that the user's finger actually intends to slide upwards, but due to inertia, it slides downwards first and then upwards. To identify this type of sliding, the similarity threshold set can include: a change-direction comparison threshold and a high-confidence comparison threshold, where the change-direction comparison threshold < the high-confidence comparison threshold; the offset distance threshold set can include: a jitter displacement threshold, a high-confidence displacement threshold, and a change-direction displacement threshold, where the jitter displacement threshold < the high-confidence displacement threshold < the change-direction displacement threshold; the confidence classification result can include: a high-confidence result, a medium-confidence result, and a low-confidence result; correspondingly, step 5031 can specifically include the following steps: steps 50311, 50312, 50313, and 50314;

[0096] In step 50311, if the target similarity and target centroid offset satisfy the first condition after being compared with the similarity threshold set and the offset distance threshold set, respectively, the target similarity and target centroid offset are determined to be high-confidence results.

[0097] In this embodiment of the application, the directional comparison threshold in the similarity threshold set is used to determine whether the user's finger has slid in the image similarity dimension. The high confidence comparison threshold in the similarity threshold set is used to determine whether the user's finger slid amplitude is large in the image similarity dimension.

[0098] In this embodiment, the jitter displacement threshold in the offset distance threshold set is used to determine, in the image centroid offset dimension, whether the user's finger is stationary or habitually rubbing in a way that is inclined to move downwards before upwards, or vice versa. The high-confidence displacement threshold in the offset distance threshold set is used to determine, in the image centroid offset dimension, whether the user's finger undergoes a directional slide. The directional displacement threshold in the offset distance threshold set is used to determine, in the image centroid offset dimension, whether the amplitude of the directional slide of the user's finger is large.

[0099] In this embodiment of the application, the direction information of the target centroid offset can be represented by "+" or "-". For example, in the scenario of vertical sliding, "+" represents sliding upward and "-" represents sliding downward.

[0100] In this embodiment of the application, the high confidence result is used to characterize the probability that the user's finger slides in a different direction on the fingerprint collection area.

[0101] In this application embodiment, the first condition may include one of the following sub-conditions: target similarity > high confidence comparison threshold and target centroid offset distance > change displacement threshold; target similarity > high confidence comparison threshold and target centroid offset distance > jitter displacement threshold and the sliding direction represented by the direction information of the target centroid offset in the current control cycle is the same as the sliding direction determined in the previous control cycle.

[0102] In step 50312, if the target similarity and target centroid offset satisfy the second condition after being compared with the similarity threshold set and the offset distance threshold set, respectively, the target similarity and target centroid offset are determined to be medium confidence results.

[0103] In this embodiment of the application, the medium confidence result is used to characterize the high probability that the user's finger slides in a different direction on the fingerprint collection area.

[0104] In this embodiment of the application, the second condition may include one of the following sub-conditions: target similarity > high confidence comparison threshold and target centroid offset distance > high confidence displacement threshold, target similarity > change direction comparison threshold and target centroid offset distance > change direction displacement threshold, target similarity > change direction comparison threshold and the sum of centroid offset distances in the same sliding direction in the most recent T control cycles > change direction displacement threshold, T≥2.

[0105] In step 50313, if the target similarity and target centroid offset do not meet the first and second conditions after being compared with the similarity threshold set and the offset distance threshold set, respectively, the target similarity and target centroid offset are determined to be low-confidence results.

[0106] In this embodiment of the application, the low confidence result is used to characterize that the probability of the user's finger sliding in a different direction on the fingerprint collection area is very low.

[0107] In step 504, based on the confidence classification results of the target similarity and the target centroid offset, the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle is determined.

[0108] In this embodiment of the application, the motion state may include: sliding, shaking, or stillness; wherein, shaking is rubbing, and sliding includes: sliding in the same direction or sliding in the opposite direction compared to the previous control cycle in the current control cycle.

[0109] In this embodiment, since both high-confidence and medium-confidence results indicate a high probability that the user's finger slid in a different direction on the fingerprint acquisition area, while low-confidence results indicate a low probability, a further determination is made as to whether the finger actually slid in a different direction for high-confidence and medium-confidence results. For low-confidence results, it is directly determined that the finger did not slid in a different direction.

[0110] In this embodiment, when the target similarity and target centroid offset are high-confidence or medium-confidence results, for the first control cycle, if the target centroid offset distance is 0, the first motion state of the finger at the execution time of the target algorithm within the first control cycle is determined to be stationary; if 0 < target centroid offset distance < jitter displacement threshold, the first motion state of the finger at the execution time of the target algorithm within the first control cycle is determined to be jittering; if the target centroid offset distance > jitter displacement threshold, the first motion state of the finger at the execution time of the target algorithm within the first control cycle is determined to be sliding.

[0111] In this embodiment, when the target similarity and target centroid offset are high-confidence or medium-confidence results, for the j-th control cycle after the first control cycle (j≥2), if the sliding direction represented by the direction information of the target centroid offset in the j-th control cycle is the same as the sliding determined in the (j-1)-th control cycle and the target similarity is greater than the jitter displacement threshold, then the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is determined to be sliding in the same direction; if the sliding direction represented by the direction information of the target centroid offset in the j-th control cycle is different from the sliding determined in the (j-1)-th control cycle and the target centroid offset distance is greater than the change-direction displacement threshold, then the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is determined to be sliding in the opposite direction; otherwise, the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is determined to be stationary or jittering.

[0112] In this embodiment, when the target similarity and target centroid offset are low-confidence results, the i-th motion state of the finger at the execution time point of the target algorithm in the i-th control cycle is determined to be either stationary or shaking.

[0113] In this embodiment, when the finger's motion state at the i-th execution time point of the target algorithm within the i-th control cycle is sliding, the target centroid offset distance is compensated according to a preset displacement compensation amount, wherein the preset displacement compensation amount is greater than 0. The compensated target centroid offset distance is used to determine the (i+1)-th motion state of the finger at the (i+1)-th execution time point of the target algorithm within the (i+1)-th control cycle. Displacement compensation ensures the accuracy of the motion state in subsequent control cycles.

[0114] In this embodiment, confidence level classification is performed based on the target algorithm processing results to determine the sliding direction of the finger and adjust displacement compensation. This can improve the accuracy of direction change judgment and the uniformity of displacement adjustment, thereby improving the smoothness and sensitivity of navigation control of the controlled program of the electronic device.

[0115] As can be seen from the above embodiments, in this embodiment, the fingerprint image to be processed is acquired by the fingerprint sensor corresponding to the i-th control cycle; where i≥1; at the execution time point of the target algorithm in the i-th control cycle, the fingerprint image to be processed is processed according to the target algorithm to obtain the target algorithm processing result in the i-th control cycle; wherein, the target algorithm processing result includes: target similarity and target centroid offset, the target centroid offset includes: target centroid offset distance and direction information, the target centroid offset distance is used to characterize the sliding distance of the finger on the fingerprint acquisition area, and the direction information is used to characterize the sliding direction of the finger on the fingerprint acquisition area; since the similarity and centroid offset between two fingerprint images can reflect the motion state of the user's finger on the fingerprint acquisition area, and the confidence level is an indicator for measuring the reliability of the prediction result, the target similarity and target centroid offset are classified by confidence level. According to the confidence level classification result to which the target similarity and target centroid offset belong, the i-th motion state of the finger at the execution time point of the target algorithm in the i-th control cycle can be accurately determined, thereby realizing the navigation control of the controlled program of the electronic device in accordance with the user's expectations.

[0116] In addition, from Figure 4BAs can be seen, besides the initial image capture, there is an algorithm processing time interval between each adjacent image capture by the fingerprint sensor. For example, there is an algorithm processing time interval between the second and third image captures, and also between the third and fourth image captures. In scenarios where the finger slides quickly across the fingerprint capture area, the low overlap between adjacent fingerprint images captured by the fingerprint sensor can lead to inaccurate judgment of the finger's movement, resulting in sluggish navigation control response or navigation control of the electronic device's controlled program failing to meet user expectations.

[0117] To address the aforementioned technical problems, in some other embodiments provided in this application, the fingerprint images to be processed for each control cycle are set in the following manner:

[0118] The fingerprint images to be processed corresponding to the first control cycle include: one fingerprint image acquired at the first pre-acquisition time point within the first control cycle and one fingerprint image acquired at the second pre-acquisition time point; the fingerprint images to be processed corresponding to each control cycle after the first control cycle include: one fingerprint image acquired at the last acquisition time point within the previous control cycle and one fingerprint image acquired at the first pre-acquisition time point within the current control cycle; each control cycle includes, in chronological order: the pre-acquisition time point, the execution time point of the target algorithm, and at least one candidate acquisition time point; the acquisition time point includes the pre-acquisition time point and the candidate acquisition time point, i≥1.

[0119] In this embodiment, when a finger effectively touches the fingerprint acquisition area, a fingerprint image acquisition control cycle is entered. Specifically, the entire navigation control process of the controlled program of the electronic device is divided into a series of control cycles. At the beginning of each control cycle, the sliding distance and sliding direction of the finger on the fingerprint acquisition area are calculated based on at least two adjacent fingerprint images continuously acquired by the fingerprint sensor. The actual length of the control cycle is determined based on the sliding distance of the finger. Throughout the control cycle, the navigation control of the controlled program of the electronic device is always performed based on the finger movement state calculated at the beginning.

[0120] In some embodiments, in order to reduce the amount of computation, increase the computation speed, and thus increase the reporting frequency and improve the response speed, the number of fingerprint images to be processed in each control cycle can be two.

[0121] In this embodiment, since the fingerprint sensor has no previous fingerprint image to refer to when it first acquires an image, it acquires images twice consecutively during the first control cycle. At this time, the fingerprint images to be processed corresponding to the first control cycle include: a fingerprint image acquired at the first pre-acquisition time point within the first control cycle and a fingerprint image acquired at the second pre-acquisition time point. The first control cycle includes, in chronological order: the first pre-acquisition time point, the second pre-acquisition time point, the execution time point of the target algorithm, and at least one candidate acquisition time point. The first pre-image acquisition time point of the first control cycle is the time point when the fingerprint sensor first acquires an image. The second pre-image acquisition time point is separated from the first pre-image acquisition time point by one fingerprint sensor acquisition duration. The execution time point of the target algorithm is separated from the second pre-image acquisition time point by one fingerprint sensor acquisition duration. That is, the first pre-image acquisition time point, the second pre-image acquisition time point, and the execution time point of the target algorithm of the first control cycle are all known. It is necessary to solve for the number of candidate image acquisition time points in the first control cycle. After solving for the number of candidate image acquisition time points, the actual length of the first control cycle is determined.

[0122] In this embodiment, the target algorithm is processed only once in each control cycle, which reduces the execution frequency of the algorithm and thus reduces the power consumption of the electronic device.

[0123] In this embodiment, the fingerprint image to be processed for each control cycle after the first control cycle includes: a fingerprint image acquired at the last candidate acquisition time point in the previous control cycle and a fingerprint image acquired at the first pre-acquisition time point in the current control cycle. Each control cycle after the first control cycle includes, in chronological order, the first pre-acquisition time point, the execution time point of the target algorithm, and at least one candidate acquisition time point. The interval between two adjacent candidate acquisition time points is the time required for the fingerprint sensor to acquire one fingerprint image. After the actual length of the first control cycle is determined, the starting time point of the second control cycle is determined. With the starting time point of the second control cycle determined, the first pre-acquisition time point and the execution time point of the target algorithm in the second control cycle are also determined. Given that the first pre-acquisition time point and the execution time point of the target algorithm in the second control cycle are known, it is necessary to solve for the number of candidate acquisition time points in the second control cycle. After solving for the number of candidate acquisition time points, the actual length of the second control cycle is determined. The method for determining the actual length of each control cycle after the second control cycle is similar to the method for determining the actual length of the second control cycle.

[0124] For example, such as Figure 7As shown, when a finger effectively touches the fingerprint acquisition area, the entire navigation control process of the controlled program of the electronic device is divided into a series of control cycles. Starting from the first control cycle, the first control cycle includes, in chronological order: the "first image acquisition" time point (i.e., the first pre-image acquisition time point), the "second image acquisition" time point (i.e., the second pre-image acquisition time point), the "first algorithm processing" time point (i.e., the execution time point of the target algorithm), and Z1 "candidate image acquisition" time points (i.e., at least one candidate image acquisition time point). Z1 is a value that needs to be solved or can be a fixed value. The processor of the electronic device controls the fingerprint sensor to acquire a fingerprint image at the "first image acquisition" time point of the first control cycle and acquire another fingerprint image at the "second image acquisition" time point. The processor performs the first algorithm processing on these two fingerprint images at the "first algorithm processing" time point, and determines Z1 based on the processing result of the first algorithm processing. For ease of understanding, Figure 7 In the example of Z1=4, the first control cycle includes the following time points in chronological order: the "first image acquisition" time point, the "second image acquisition" time point, the "first algorithm processing" time point, and four "candidate image acquisition" time points.

[0125] In the second control cycle, which includes the following time sequence: the "first image acquisition" time point (i.e., the first pre-image acquisition time point), the "second algorithm processing" time point (i.e., the execution time point of the target algorithm), and Z2 "candidate image acquisition" time points (i.e., at least one candidate image acquisition time point). Z2 is a value that needs to be solved or can be a fixed value. The processor of the electronic device controls the fingerprint sensor to acquire a fingerprint image at the "first image acquisition" time point of the second control cycle. At the "second algorithm processing" time point, the processor performs a second algorithm processing on the fingerprint image acquired at the "first image acquisition" time point of the second control cycle and the fingerprint image acquired at the "fourth candidate image acquisition" time point of the first control cycle (i.e., the last image acquisition time point). Z2 is determined based on the processing result of the second algorithm processing. For ease of understanding, Figure 7 Taking Z2=4 as an example, the second control cycle includes, in chronological order, the following time points: the "first image acquisition" time point, the "second algorithm processing" time point, and four "candidate image acquisition" time points. Figure 7 The first, second, and third algorithm processing steps are all target algorithm processing steps.

[0126] In some embodiments, considering that the more fingerprint images used by the algorithm, the more accurate the calculation results are usually, in order to ensure the accuracy of the algorithm's calculation results, the number of fingerprint images to be processed in each control cycle can be more than two.

[0127] In this embodiment, the fingerprint image to be processed corresponding to the first control cycle may include two or more fingerprint images acquired at multiple previous acquisition time points. Accordingly, the target algorithm processes two or more fingerprint images each time.

[0128] In this embodiment, the fingerprint images to be processed for each control cycle after the first control cycle include, in addition to: one fingerprint image acquired at the last acquisition time point in the previous control cycle and one fingerprint image acquired at the first preceding acquisition time point in the current control cycle, at least two fingerprint images acquired at at least two candidate acquisition time points in the previous control cycle. Accordingly, the target algorithm processes more than two fingerprint images each time.

[0129] In some embodiments, the fingerprint sensor can be controlled to acquire images at each candidate acquisition time point within the i-th control cycle.

[0130] Optionally, the fingerprint image acquired at the last candidate acquisition time point within the i-th control cycle is used to determine the i+1-th motion state of the finger at the execution time point of the target algorithm within the i+1-th control cycle.

[0131] For example, such as Figure 7 As shown, for the first control cycle, fingerprint images are acquired at the four candidate image acquisition time points within the first control cycle: the "first candidate image acquisition" time point, the "second candidate image acquisition" time point, the "third candidate image acquisition" time point, and the "fourth candidate image acquisition" time point. Among them, the fingerprint image acquired at the "fourth candidate image acquisition" time point within the first control cycle is used to determine the second motion state of the finger at the execution time point of the target algorithm within the second control cycle.

[0132] Optionally, at least two fingerprint images acquired at least two candidate image acquisition time points within the i-th control cycle are used to determine the i+1-th motion state of the finger at the execution time point of the target algorithm within the i+1-th control cycle.

[0133] For example, such as Figure 7 As shown, for the first control cycle, fingerprint images are acquired at the four candidate image acquisition time points within the first control cycle: the "first candidate image acquisition" time point, the "second candidate image acquisition" time point, the "third candidate image acquisition" time point, and the "fourth candidate image acquisition" time point. Among them, the two fingerprint images acquired at the "third candidate image acquisition" time point and the "fourth candidate image acquisition" time point within the first control cycle are used to determine the second motion state of the finger at the execution time point of the target algorithm within the second control cycle.

[0134] In this embodiment of the application, although fingerprint images are acquired at each candidate acquisition time point, not all acquired candidate fingerprint images are processed by the algorithm, which can reduce processing time.

[0135] In some embodiments, the fingerprint sensor can be controlled to acquire an image at the last candidate acquisition time point in the i-th control cycle; wherein, the fingerprint image acquired at the last candidate acquisition time point in the i-th control cycle is used to determine the i+1th motion state of the finger at the execution time point of the target algorithm in the i+1th control cycle.

[0136] For example, such as Figure 7 As shown, for the first control cycle, there are four candidate image acquisition time points within the first control cycle: the "first candidate image acquisition" time point, the "second candidate image acquisition" time point, the "third candidate image acquisition" time point, and the "fourth candidate image acquisition" time point. The fingerprint sensor is controlled to only acquire images at the "fourth candidate image acquisition" time point. At the remaining three candidate image acquisition time points: the "first candidate image acquisition" time point, the "second candidate image acquisition" time point, and the "third candidate image acquisition" time point, the fingerprint sensor is controlled to not work and does not acquire images. Among them, the fingerprint image acquired at the "fourth candidate image acquisition" time point within the first control cycle is used to determine the second motion state of the finger at the execution time point of the target algorithm within the second control cycle.

[0137] In this embodiment, considering that each candidate image acquisition time point in the i-th control cycle uses the finger movement state initially determined in the control cycle, there is no need to perform algorithm processing at each candidate image acquisition time point. Since only the (i+1)-th control cycle uses the fingerprint image acquired at the last candidate image acquisition time point in the i-th control cycle, for all candidate image acquisition time points in the i-th control cycle, the fingerprint sensor can be controlled to only acquire images at the last candidate image acquisition time point, and not work at other candidate image acquisition time points, thereby reducing the image acquisition power consumption of the fingerprint sensor without affecting the next control cycle.

[0138] In this embodiment, since the i-th motion state is determined based on the result of the target algorithm, and the result of the target algorithm is calculated at the execution time point of the target algorithm within the i-th control cycle, and the execution time point of the target algorithm is located in the early stage of the i-th control cycle, for ease of understanding and description, the i-th motion state can be referred to as the initial motion state within the i-th control cycle. The initial motion state is used to perform navigation control on the controlled program of the electronic device throughout the entire i-th control cycle.

[0139] In this embodiment, from the perspective of predicting the future sliding tendency of the finger, for the i-th control cycle, the larger the sliding distance determined at the beginning of the control cycle, the higher the sliding quality of the user's finger, and the higher the probability that the user will continue to slide. In this case, the same motion state (i.e., the i-th motion state) can be used for a longer period of time. The smaller the sliding distance determined at the beginning of the control cycle, the lower the sliding quality of the user's finger, and the lower the probability that the user will continue to slide. In this case, the same motion state can be used for a shorter period of time. The subsequent duration mentioned above can be represented by the number of candidate image acquisition time points. The subsequent duration = the duration of the fingerprint sensor acquiring a fingerprint image * the number of candidate image acquisition time points.

[0140] In some embodiments, in order to reduce computational load, the number of candidate image acquisition time points in each control cycle can be a fixed value.

[0141] In some embodiments, to improve navigation response speed, the number of candidate image acquisition time points in each control cycle is positively correlated with the sliding distance of the finger on the fingerprint acquisition area.

[0142] In some embodiments, the number of candidate image acquisition time points in each control cycle is determined based on the sliding distance of the finger on the fingerprint acquisition area and the set of candidate numbers in that control cycle; wherein, the set of candidate numbers is {1,…,K}, the number of candidate image acquisition time points in the i-th control cycle is positively correlated with the sliding distance of the finger on the fingerprint acquisition area, and K≥2.

[0143] In this embodiment, research indicates that responding to user input within 100 milliseconds is generally perceived by humans as an immediate response. Any longer, and the connection between the operation and the response is broken, leading to a perceived delay. For example, if a user clicks a button and receives a response within 100 milliseconds, the user perceives the response as timely and without any noticeable delay. Therefore, by dynamically adjusting the number of candidate image acquisition points within the i-th control cycle by controlling the finger's sliding distance across the fingerprint acquisition area, the navigation response speed can be adjusted.

[0144] In some embodiments, K is determined based on the upper limit of user response time, the time required for the fingerprint sensor to acquire a fingerprint image, and the number of fingerprint images used by the target algorithm each time.

[0145] In this embodiment of the application, K = (user response time - algorithm processing time - number of images used by the algorithm * single image acquisition time) / reporting interval time, where the reporting interval time is equal to the single image acquisition time.

[0146] For example, when the user response time = 100ms, the number of images used by the algorithm = 2, the single image acquisition time = 10ms, the reporting interval time = 10ms, and the algorithm processing time = 40ms, K = (100 - 40 - 2 * 10) / 10 = 4.

[0147] In this embodiment, the displacement distribution of the dataset collected from the effective touch fingerprint collection area of ​​different users can be used to set multiple centroid offset distance thresholds. Using these thresholds, the sliding distance of the user's finger is divided into K levels. If the sliding distance is in the first level, the number of candidate image collection time points is 1. If the sliding distance is in the second level, the number of candidate image collection time points is 2, ..., if the sliding distance is in the Kth level, the number of candidate image collection time points is K.

[0148] For example, K=4, meaning there are four levels. The distance threshold corresponding to the first level is [0,10) mm, the distance threshold corresponding to the second level is [10,20) mm, the distance threshold corresponding to the third level is [20,30) mm, and the threshold corresponding to the fourth level is [30~L) mm, where L is the length of the fingerprint collection area.

[0149] For ease of understanding, combined with Figure 8 To illustrate, in Figure 8 The target algorithm processing result is calculated at the first algorithm processing time point of the first control cycle. This target algorithm processing result indicates that the user's finger sliding distance is large, that is, the sliding quality of the user's finger on the fingerprint collection area is relatively high. At this time, the number of candidate image acquisition time points in the first control cycle is set to 4. The finger movement state reported at these 4 candidate image acquisition time points is the same as the finger movement state reported at the first algorithm processing time point. A total of 5 reports are made in the entire first control cycle, and the reporting frame rate can reach 50fps.

[0150] exist Figure 8 The target algorithm processing result is calculated at the second algorithm processing time point of the second control cycle. The target algorithm processing result indicates that the sliding distance of the user's finger is less than the sliding distance of the first control cycle, that is, the sliding quality of the user's finger on the fingerprint acquisition area has decreased. At this time, the number of candidate image acquisition time points in the second control cycle is set to 3. The finger movement state reported at these 3 candidate image acquisition time points is the same as the finger movement state reported at the second algorithm processing time point. A total of 4 reports are made in the entire second control cycle.

[0151] exist Figure 8The target algorithm processing result is calculated at the third algorithm processing time point of the third control cycle. This target algorithm processing result indicates that the sliding distance of the user's finger is less than the sliding distance of the second control cycle, that is, the sliding quality of the user's finger on the fingerprint acquisition area has decreased significantly. At this time, the number of candidate image acquisition time points in the third control cycle is set to 1. The finger movement state reported at this candidate image acquisition time point is the same as the finger movement state reported at the third algorithm processing time point. A total of 2 reports are made in the entire third control cycle.

[0152] In this embodiment, the number of candidate image acquisition time points in the i-th control cycle is selected from the candidate quantity set {1,…,K} based on the sliding distance of the finger on the fingerprint acquisition area. This can reduce the power consumption of the processor and fingerprint sensor of the electronic device while ensuring accurate navigation control.

[0153] Figure 9 This is a flowchart of a navigation control method provided in an embodiment of this application, applied to an electronic device, which includes a fingerprint sensor, such as... Figure 9 As shown, the method may include the following steps: step 901 and step 902;

[0154] In step 901, the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle is determined.

[0155] In this embodiment, the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle can be determined according to the aforementioned method for determining fingerprint motion state, which will not be elaborated further here.

[0156] In step 902, at each candidate image acquisition time point within the i-th control cycle, the i-th navigation control signal for the electronic device is generated based on the i-th motion state.

[0157] In this embodiment of the application, a navigation control signal is generated based on the reported finger movement state. In addition to generating a navigation control signal at each candidate image acquisition time point, when the i-th movement state is determined based on the target algorithm processing result, the i-th navigation control signal for the electronic device is also generated based on the i-th movement state.

[0158] In this embodiment, the navigation control signal is used to perform navigation control on the controlled program of the electronic device.

[0159] For example, such as Figure 8 As shown, in Figure 8The first algorithm processing time point of the first control cycle calculates the target algorithm processing result. If the target algorithm processing result determines that the user's finger movement state is swiping upwards, then in the five reports of the first control cycle, "finger movement state is swiping upwards" is reported. During the first control cycle, the processor performs navigation control of the electronic device's controlled program according to the finger swiping upwards. Figure 8 The second algorithm processing time point of the second control cycle is used to calculate the target algorithm processing result. If the user's finger movement state is determined to be downward according to the target algorithm processing result, then "finger movement state is downward" will be reported in all 4 reports of the second control cycle. During the second control cycle, the processor will perform navigation control on the controlled program of the electronic device according to the finger downward movement.

[0160] As can be seen, in this embodiment, the navigation control process of the controlled program of the electronic device can be divided into multiple control cycles. For each control cycle, the target algorithm processing result is calculated using the adjacent fingerprint images corresponding to that control cycle. Based on the target algorithm processing result, the movement state of the finger in the fingerprint acquisition area under that control cycle is determined. At each candidate image acquisition time point within that control cycle, navigation control is performed on the controlled program of the electronic device according to the movement state of the finger within that control cycle. Only one target algorithm processing is performed within each control cycle, and one target algorithm processing result yields one target algorithm processing result, which determines one movement state. Since the fingerprint images used in one target algorithm processing of each control cycle are adjacent fingerprint images continuously acquired by the fingerprint sensor, there is no interval between these fingerprint images and the processor's algorithm processing time. Therefore, the content overlap between these fingerprint images is high, which can accurately determine the movement state of the finger in the fingerprint acquisition area. Each candidate image acquisition time point within that control cycle uses the same running state, which can increase the reporting frequency and improve the response speed. Especially during rapid sliding, it avoids navigation control response lag and realizes navigation control of the controlled program of the electronic device that meets the user's expectations within each control cycle.

[0161] Figure 10 This is a schematic diagram of a device for determining the movement state of a fingerprint, provided in an embodiment of this application. It is applied to an electronic device, which includes a fingerprint sensor, such as... Figure 10 As shown, the device 1000 for determining the movement state of a fingerprint may include:

[0162] The acquisition module 1001 is used to acquire the fingerprint image to be processed collected by the fingerprint sensor corresponding to the i-th control cycle; where i≥1;

[0163] The processing module 1002 is used to process the fingerprint image to be processed according to the target algorithm at the execution time point of the target algorithm in the i-th control cycle, and obtain the target algorithm processing result in the i-th control cycle; wherein, the target algorithm processing result includes: target similarity and target centroid offset, the target centroid offset includes: target centroid offset distance and direction information, the target centroid offset distance is used to characterize the sliding distance of the finger on the fingerprint acquisition area, and the direction information is used to characterize the sliding direction of the finger on the fingerprint acquisition area;

[0164] The classification module 1003 is used to perform confidence classification on the target similarity and the target centroid offset to obtain the confidence classification result to which the target similarity and the target centroid offset belong;

[0165] The first determining module 1004 is used to determine the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle based on the target similarity and the confidence classification result to which the target centroid offset belongs.

[0166] As can be seen from the above embodiments, in this embodiment, the fingerprint image to be processed is acquired by the fingerprint sensor corresponding to the i-th control cycle; where i≥1; at the execution time point of the target algorithm in the i-th control cycle, the fingerprint image to be processed is processed according to the target algorithm to obtain the target algorithm processing result in the i-th control cycle; wherein, the target algorithm processing result includes: target similarity and target centroid offset, the target centroid offset includes: target centroid offset distance and direction information, the target centroid offset distance is used to characterize the sliding distance of the finger on the fingerprint acquisition area, and the direction information is used to characterize the sliding direction of the finger on the fingerprint acquisition area; since the similarity and centroid offset between two fingerprint images can reflect the motion state of the user's finger on the fingerprint acquisition area, and the confidence level is an indicator for measuring the reliability of the prediction result, the target similarity and target centroid offset are classified by confidence level. According to the confidence level classification result to which the target similarity and target centroid offset belong, the i-th motion state of the finger at the execution time point of the target algorithm in the i-th control cycle can be accurately determined, thereby realizing the navigation control of the controlled program of the electronic device in accordance with the user's expectations.

[0167] Optionally, as an embodiment, the classification module 1003 is specifically used to perform confidence classification on the target similarity and the target centroid offset according to a similarity threshold set and an offset distance threshold set, to obtain the confidence classification result to which the target similarity and the target centroid offset belong; wherein, the similarity threshold set includes at least one threshold, and the threshold in the similarity threshold set is used to perform confidence classification on the target similarity; the offset distance threshold set includes at least one threshold, and the threshold in the offset distance threshold set is used to perform confidence classification on the target centroid offset.

[0168] Optionally, as an embodiment, the similarity threshold set may include: a directional comparison threshold and a high-confidence comparison threshold, wherein the directional comparison threshold < the high-confidence comparison threshold; the offset distance threshold set may include: a jitter displacement threshold, a high-confidence displacement threshold, and a directional displacement threshold, wherein the jitter displacement threshold < the high-confidence displacement threshold < the directional displacement threshold; the confidence classification result may include: a high-confidence result, a medium-confidence result, and a low-confidence result;

[0169] The classification module 1003 is specifically used to determine that the target similarity and the target centroid offset belong to high confidence results when the target similarity and the target centroid offset satisfy a first condition after being compared with the similarity threshold set and the offset distance threshold set, respectively; to determine that the target similarity and the target centroid offset belong to medium confidence results when the target similarity and the target centroid offset satisfy a second condition after being compared with the similarity threshold set and the offset distance threshold set, respectively; and to determine that the target similarity and the target centroid offset belong to low confidence results when the target similarity and the target centroid offset do not satisfy the first condition and the second condition after being compared with the similarity threshold set and the offset distance threshold set, respectively.

[0170] Optionally, as an embodiment, the first condition may include one of the following sub-conditions: target similarity > high confidence comparison threshold and target centroid offset distance > change direction displacement threshold; target similarity > high confidence comparison threshold and target centroid offset distance > jitter displacement threshold and the sliding direction represented by the direction information of the target centroid offset in the current control cycle is the same as the sliding direction determined in the previous control cycle; the second condition may include one of the following sub-conditions: target similarity > high confidence comparison threshold and target centroid offset distance > high confidence displacement threshold; target similarity > change direction comparison threshold and target centroid offset distance > change direction displacement threshold; target similarity > change direction comparison threshold and the sum of centroid offset distances in the same sliding direction in the most recent T control cycles > change direction displacement threshold, T≥2.

[0171] Optionally, as an embodiment, the motion state includes: sliding, shaking, or standing still; the sliding includes: sliding in the same direction or sliding in the opposite direction compared to the previous control cycle in the current control cycle.

[0172] Optionally, as an embodiment, the first determining module 1004 is specifically used to determine, when the target similarity and the target centroid offset are high-confidence or medium-confidence results, if the sliding direction represented by the direction information of the target centroid offset in the j-th control cycle is the same as the sliding determined in the (j-1)-th control cycle and the target similarity is greater than the jitter displacement threshold, then determine that the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is sliding in the same direction; if the sliding direction represented by the direction information of the target centroid offset in the j-th control cycle is the same as the sliding determined in the (j-1)-th control cycle and the target similarity is greater than the jitter displacement threshold, then determine that the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is sliding in the same direction; if the sliding direction represented by the direction information of the target centroid offset in the j-th control cycle is... If the direction of movement is different from the sliding determined in the (j-1)th control cycle and the target centroid offset distance is greater than the directional displacement threshold, then the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is determined to be reverse sliding; otherwise, the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is determined to be stationary or jittering, where j≥2; if the target similarity and the target centroid offset are low confidence results, then the i-th motion state of the finger at the execution time point of the target algorithm in the i-th control cycle is determined to be stationary or jittering.

[0173] Optionally, as an embodiment, the first determining module 1004 is specifically used to, when the target similarity and the target centroid offset are high-confidence results or medium-confidence results, for the first control cycle, if the target centroid offset distance is 0, then determine that the first motion state of the finger at the execution time point of the target algorithm in the first control cycle is stationary; if 0 < target centroid offset distance < jitter displacement threshold, then determine that the first motion state of the finger at the execution time point of the target algorithm in the first control cycle is jittering; if the target centroid offset distance > jitter displacement threshold, then determine that the first motion state of the finger at the execution time point of the target algorithm in the first control cycle is sliding.

[0174] Optionally, as an embodiment, the device 1000 for determining the fingerprint movement state may further include:

[0175] The compensation module is used to compensate the target centroid offset distance according to a preset displacement compensation amount when the finger is sliding at the i-th motion state of the target algorithm execution time point in the i-th control cycle. The preset displacement compensation amount is greater than 0, and the compensated target centroid offset distance is used to determine the i+1-th motion state of the finger at the i+1-th motion state of the target algorithm execution time point in the i+1-th control cycle.

[0176] Optionally, as an embodiment, the processing module 1002 is specifically configured to, when the fingerprint sensor is configured with personalized image acquisition parameters, perform interpolation processing on all fingerprint images to be processed to obtain a full-size fingerprint image; perform background removal processing on the full-size fingerprint image to obtain a background-removed fingerprint image; extract fingerprint features from the background-removed fingerprint image to obtain at least two sets of fingerprint features; wherein, one fingerprint image corresponds to one set of fingerprint features; align the at least two sets of fingerprint features to obtain at least two aligned sets of fingerprint features; calculate at least one set of candidate similarity and candidate centroid offset based on each pair of aligned fingerprint features; when the number of fingerprint images included in the fingerprint image to be processed is equal to two, determine the calculated set of candidate similarity and candidate centroid offset as the target similarity and target centroid offset, respectively; when the number of fingerprint images included in the fingerprint image to be processed is greater than two, select the largest from the at least one set of candidate similarity and candidate centroid offset as the target similarity and target centroid offset, respectively.

[0177] Optionally, as an embodiment, the fingerprint image to be processed corresponding to the first control cycle includes: a fingerprint image acquired at the first pre-acquisition time point within the first control cycle and a fingerprint image acquired at the second pre-acquisition time point; the fingerprint image to be processed corresponding to each control cycle after the first control cycle includes: a fingerprint image acquired at the last acquisition time point within the previous control cycle and a fingerprint image acquired at the first pre-acquisition time point within the current control cycle; each control cycle includes, in chronological order: a pre-acquisition time point, the execution time point of the target algorithm, and at least one candidate acquisition time point; the acquisition time point includes the pre-acquisition time point and the candidate acquisition time point; the number of candidate acquisition time points within each control cycle is determined based on the target centroid offset distance and the candidate quantity set; wherein, the candidate quantity set is {1,…,K}, the number of candidate acquisition time points within the i-th control cycle is positively correlated with the target centroid offset distance, and K≥2.

[0178] Optionally, as an example, K is determined based on the upper limit of user response time, the time required for the fingerprint sensor to acquire a fingerprint image, and the number of fingerprint images used by the target algorithm each time.

[0179] Figure 11 This is a schematic diagram of a navigation control device provided in an embodiment of the present application. It is applied to an electronic device, which includes a fingerprint sensor. The navigation control device 1100 may include: a second determining module 1101 and a control module 1102.

[0180] The second determining module 1101 is used to determine the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle.

[0181] The control module 1102 is used to generate an i-th navigation control signal for the electronic device based on the i-th motion state at each candidate image acquisition time point within the i-th control cycle.

[0182] As can be seen, in this embodiment, the navigation control process of the controlled program of the electronic device can be divided into multiple control cycles. For each control cycle, the target algorithm processing result is calculated using the adjacent fingerprint images corresponding to that control cycle. Based on the target algorithm processing result, the movement state of the finger in the fingerprint acquisition area under that control cycle is determined. At each candidate image acquisition time point within that control cycle, navigation control is performed on the controlled program of the electronic device according to the movement state of the finger within that control cycle. Only one target algorithm processing is performed within each control cycle, and one target algorithm processing result yields one target algorithm processing result, which determines one movement state. Since the fingerprint images used in one target algorithm processing of each control cycle are adjacent fingerprint images continuously acquired by the fingerprint sensor, there is no interval between these fingerprint images and the processor's algorithm processing time. Therefore, the content overlap between these fingerprint images is high, which can accurately determine the movement state of the finger in the fingerprint acquisition area. Each candidate image acquisition time point within that control cycle uses the same running state, which can increase the reporting frequency and improve the response speed. Especially during rapid sliding, it avoids navigation control response lag and realizes navigation control of the controlled program of the electronic device that meets the user's expectations within each control cycle.

[0183] Any step and specific operation within any step in the method embodiments provided in this application can be completed by the corresponding module in the device embodiments. The process of each module in the device performing its corresponding operation is described in the method embodiments. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple; relevant details can be found in the descriptions of the method embodiments.

[0184] Figure 12 This is a structural block diagram of an electronic device provided in an embodiment of this application. The electronic device includes a processing component 1222, which further includes one or more processors, and memory resources represented by a memory 1232 for storing instructions executable by the processing component 1222, such as application programs. The application programs stored in the memory 1232 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1222 is configured to execute instructions to perform the aforementioned methods for determining fingerprint motion states and navigation control methods.

[0185] The electronic device may also include a fingerprint sensor 1224 for acquiring fingerprint images.

[0186] The electronic device may also include a power supply component 1226 configured to perform power management of the electronic device, a wired or wireless network interface 1250 configured to connect the electronic device to a network, and an input / output (I / O) interface 1258. The electronic device may operate on an operating system stored in memory 1232, such as Windows Server™, MacOS X™, Unix™, Linux™, FreeBSD™, or similar.

[0187] According to yet another embodiment of this application, this application also provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps in the method for determining fingerprint motion state and the navigation control method as described in any of the above embodiments.

[0188] According to yet another embodiment of this application, this application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps in the method for determining fingerprint motion state and the navigation control method as described in any of the above embodiments.

[0189] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0190] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0191] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0192] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. Although preferred embodiments of the embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0193] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0194] The above provides a detailed description of the method for determining fingerprint motion state, the navigation control method, and the electronic device provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for determining the motion state of a fingerprint, applied to an electronic device, the electronic device including a fingerprint sensor, characterized in that, The method includes: Obtain the fingerprint image to be processed collected by the fingerprint sensor corresponding to the i-th control cycle; where i≥1; At the execution time point of the target algorithm within the i-th control cycle, the fingerprint image to be processed is processed according to the target algorithm to obtain the target algorithm processing result within the i-th control cycle; wherein, the target algorithm processing result includes: target similarity and target centroid offset, the target centroid offset includes: target centroid offset distance and direction information, the target centroid offset distance is used to characterize the sliding distance of the finger on the fingerprint acquisition area, and the direction information is used to characterize the sliding direction of the finger on the fingerprint acquisition area; The target similarity and the target centroid offset are classified with confidence to obtain the confidence classification results to which the target similarity and the target centroid offset belong; Based on the target similarity and the confidence classification result of the target centroid offset, the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle is determined.

2. The method according to claim 1, characterized in that, The step of performing confidence classification on the target similarity and the target centroid offset to obtain the confidence classification result to which the target similarity and the target centroid offset belong includes: Based on the similarity threshold set and the offset distance threshold set, the target similarity and the target centroid offset are classified with confidence to obtain the confidence classification results of the target similarity and the target centroid offset. The similarity threshold set includes at least one threshold, which is used to classify the confidence level of the target similarity; the offset distance threshold set includes at least one threshold, which is used to classify the confidence level of the target centroid offset.

3. The method according to claim 2, characterized in that, The similarity threshold set includes: a directional comparison threshold and a high-confidence comparison threshold, wherein the directional comparison threshold is less than the high-confidence comparison threshold; The offset distance threshold set includes: jitter displacement threshold, high confidence displacement threshold and change direction displacement threshold, where jitter displacement threshold < high confidence displacement threshold < change direction displacement threshold; The confidence level classification results include: high confidence level, medium confidence level, and low confidence level. The step of classifying the target similarity and the target centroid offset with confidence based on a similarity threshold set and an offset distance threshold set, to obtain the confidence classification result to which the target similarity and the target centroid offset belong, includes: If the target similarity and the target centroid offset satisfy the first condition after being compared with the similarity threshold set and the offset distance threshold set respectively, the target similarity and the target centroid offset are determined to be high-confidence results. If the target similarity and the target centroid offset satisfy the second condition after being compared with the similarity threshold set and the offset distance threshold set, respectively, the target similarity and the target centroid offset are determined to be medium confidence results. If the target similarity and the target centroid offset do not satisfy the first condition and the second condition after being compared with the similarity threshold set and the offset distance threshold set, respectively, the target similarity and the target centroid offset are determined to be low-confidence results.

4. The method according to claim 3, characterized in that, The first condition includes one of the following sub-conditions: target similarity > high confidence comparison threshold and target centroid offset distance > change displacement threshold; target similarity > high confidence comparison threshold and target centroid offset distance > jitter displacement threshold and the sliding direction represented by the direction information of the target centroid offset in the current control cycle is the same as the sliding direction determined in the previous control cycle. The second condition includes one of the following sub-conditions: target similarity > high confidence comparison threshold and target centroid offset distance > high confidence displacement threshold; target similarity > change direction comparison threshold and target centroid offset distance > change direction displacement threshold; target similarity > change direction comparison threshold and the sum of centroid offset distances in the same sliding direction in the most recent T control cycles > change direction displacement threshold, T≥2.

5. The method according to claim 4, characterized in that, The motion states include: sliding, shaking, or standing still; the sliding includes: sliding in the same direction or sliding in the opposite direction compared to the previous control cycle in the current control cycle.

6. The method according to claim 5, characterized in that, The step of determining the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle based on the confidence classification results of the target similarity and the target centroid offset includes: If the target similarity and the target centroid offset are high-confidence or medium-confidence results, and if the sliding direction represented by the direction information of the target centroid offset in the j-th control cycle is the same as the sliding determined in the (j-1)-th control cycle and the target similarity is greater than the jitter displacement threshold, then the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is determined to be sliding in the same direction; if the sliding direction represented by the direction information of the target centroid offset in the j-th control cycle is different from the sliding determined in the (j-1)-th control cycle and the target centroid offset distance is greater than the change-direction displacement threshold, then the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is determined to be sliding in the opposite direction; otherwise, the j-th motion state of the finger at the execution time point of the target algorithm in the j-th control cycle is determined to be stationary or jittering, where j≥2; If the target similarity and the target centroid offset are low-confidence results, the i-th motion state of the finger at the execution time point of the target algorithm in the i-th control cycle is determined to be either stationary or shaking.

7. The method according to claim 6, characterized in that, The method further includes: If the target similarity and the target centroid offset are high-confidence or medium-confidence results, for the first control cycle, if the target centroid offset distance is 0, then the first motion state of the finger at the execution time of the target algorithm within the first control cycle is determined to be stationary; if 0 < target centroid offset distance < jitter displacement threshold, then the first motion state of the finger at the execution time of the target algorithm within the first control cycle is determined to be jittering; if the target centroid offset distance > jitter displacement threshold, then the first motion state of the finger at the execution time of the target algorithm within the first control cycle is determined to be sliding.

8. The method according to claim 4, characterized in that, The method further includes: When the finger is in a sliding motion state at the execution time of the target algorithm in the i-th control cycle, the target centroid offset distance is compensated according to a preset displacement compensation amount, wherein the preset displacement compensation amount is greater than 0, and the compensated target centroid offset distance is used to determine the (i+1)-th motion state of the finger at the execution time of the target algorithm in the (i+1)-th control cycle.

9. The method according to claim 1, characterized in that, The step of processing the fingerprint image to be processed according to the target algorithm to obtain the target algorithm processing result within the i-th control cycle includes: When the fingerprint sensor is configured with personalized image acquisition parameters, the fingerprint images to be processed are all interpolated to obtain full-size fingerprint images; The full-size fingerprint image is subjected to stencil removal processing to obtain a stencil-removed fingerprint image; Extract fingerprint features from the fingerprint image after removing the ridges to obtain at least two sets of fingerprint features; wherein, one fingerprint image corresponds to one set of fingerprint features; Align the at least two sets of fingerprint features to obtain at least two aligned sets of fingerprint features; Based on the aligned fingerprint features of each pair of pairs, at least one candidate similarity and candidate centroid offset are calculated respectively. When the number of fingerprint images to be processed is equal to two, the calculated set of candidate similarities and candidate centroid offsets are determined as the target similarity and target centroid offset, respectively. If the number of fingerprint images to be processed is greater than two, the largest value among the at least one set of candidate similarities and candidate centroid offsets is selected as the target similarity and target centroid offset, respectively.

10. The method according to any one of claims 1-9, characterized in that, The fingerprint images to be processed corresponding to the first control cycle include: one fingerprint image acquired at the first pre-acquisition time point within the first control cycle and one fingerprint image acquired at the second pre-acquisition time point; the fingerprint images to be processed corresponding to each control cycle after the first control cycle include: one fingerprint image acquired at the last acquisition time point within the previous control cycle and one fingerprint image acquired at the first pre-acquisition time point within the current control cycle; each control cycle includes, in chronological order: a pre-acquisition time point, the execution time point of the target algorithm, and at least one candidate acquisition time point; the acquisition time points include pre-acquisition time points and candidate acquisition time points; The number of candidate image acquisition time points in each control cycle is determined based on the target centroid offset distance and the candidate quantity set; wherein, the candidate quantity set is {1,…,K}, and the number of candidate image acquisition time points in the i-th control cycle is positively correlated with the target centroid offset distance, and K≥2.

11. The method according to claim 10, characterized in that, K is determined based on the upper limit of user response time, the time required for the fingerprint sensor to acquire a fingerprint image, and the number of fingerprint images used by the target algorithm each time.

12. A navigation control method applied to an electronic device, the electronic device including a fingerprint sensor, characterized in that, The method includes: The method for determining fingerprint motion state according to any one of claims 1-11 determines the i-th motion state of the finger at the execution time point of the target algorithm within the i-th control cycle. At each candidate image acquisition time point within the i-th control cycle, an i-th navigation control signal is generated for the electronic device based on the i-th motion state.

13. An electronic device, comprising: A fingerprint sensor, a memory, a processor, and a computer program stored in the memory, characterized in that the fingerprint sensor is used to acquire fingerprint images, and the processor executes the computer program to implement the method for determining the fingerprint motion state as described in any one of claims 1 to 11, or to implement the navigation control method as described in claim 12.

14. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When executed by a processor, the computer program / instruction implements the method for determining the fingerprint motion state as described in any one of claims 1 to 11, or the navigation control method as described in claim 12.

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