Data processing method based on biological recognition and related device

Abstract

The embodiment of the invention discloses a data processing method based on biological recognition and a related device. The method applied to the biological information identification equipment comprises the following steps: detecting the wearable equipment, and establishing communication connection with the detected wearable equipment; capturing biological information, and performing optimization processing on the captured biological information to obtain posture data in a biological information capturing process; and generating a feedback instruction based on the posture data, and sending the feedback instruction to the wearable device, so that the wearable device performs posture feedback based on the feedback instruction. According to the embodiment of the invention, the wearable device is used for reminding the biological information recognition posture of the user, so that the reminding of the biological information recognition posture does not depend on hardware configuration on the biological information recognition device any more, and the applicability is higher.

Description

Technical Field

[0001] This application relates to the field of biometrics technology, specifically to a biometrics-based data processing method and related apparatus. Background Technology

[0002] Biometric technology is a technology that closely integrates computers with high-tech methods such as optics, acoustics, biosensors, and biostatistics to identify individuals using their inherent physiological characteristics and behavioral traits. Types of biometrics mainly include facial recognition, fingerprint recognition, iris recognition, voiceprint recognition, and palmprint recognition.

[0003] Currently, feedback on whether a user's posture is correct is typically provided through biometric recognition devices. This can be achieved through methods such as displaying animations on the device screen, flashing lights, or playing audio through speakers. However, because these feedback mechanisms are usually fixed, they are unsuitable when the biometric recognition device lacks hardware such as a screen, lights, or speakers. Therefore, the applicability of biometric posture feedback solutions still needs improvement. Summary of the Invention

[0004] To address the aforementioned technical problems, embodiments of this application provide a biometric-based data processing method, a biometric-based data processing device, an electronic device, a computer-readable storage medium, and a computer program product. Embodiments of this application use wearable devices to remind users of their biometric postures, thus eliminating reliance on the hardware configuration of the biometric device and enhancing its applicability.

[0005] One aspect of this application provides a biometric data processing method applied to a biometric identification device. The method includes: detecting a wearable device and establishing a communication connection with the detected wearable device; capturing biometric information and performing optimal processing on the captured biometric information to obtain posture data during the biometric capture process; generating a feedback instruction based on the posture data and sending the feedback instruction to the wearable device, so that the wearable device provides posture feedback based on the feedback instruction.

[0006] Another aspect of this application provides a biometric data processing device configured on a biometric identification device. The device includes: a detection module configured to detect a wearable device and establish a communication connection with the detected wearable device; an optimization module configured to capture biometric information and perform optimization processing on the captured biometric information to obtain posture data during the biometric information capture process; and a processing module configured to generate a feedback instruction based on the posture data and send the feedback instruction to the wearable device, so that the wearable device provides posture feedback to the participant based on the feedback instruction.

[0007] In another exemplary embodiment, the preferred module is further configured to perform the following steps: enabling the biometric sensor to continuously capture images containing biometric information, and performing optimization on the images containing biometric information based on at least two preset image optimization conditions; continuously acquiring the distance between the biological body and the biometric sensor, and determining the offset distance of the biological body relative to the biometric sensor based on the image processing data obtained in the optimization process, so as to use the distance and the offset distance as posture data in the biometric information capture process.

[0008] In another exemplary embodiment, the preferred module is further configured to perform the following steps: if a target image that simultaneously satisfies the at least two image preferred conditions is captured, the preferred method is determined to be successful, so as to perform biometric identification based on the biometric information contained in the target image.

[0009] In another exemplary embodiment, the detection module is further configured to perform the following steps: if, based on wireless communication detection, a wireless device is detected gradually approaching the biometric identification device, then the module begins to search for available wireless devices around the biometric identification device; and establishes a communication connection with the nearest wireless device found.

[0010] In another exemplary embodiment, both the biometric identification device and the wearable device are provided with a wireless communication module with a longer communication range and a wireless communication module with a shorter communication range; the detection module is further configured to perform the following steps: activate the wireless communication module with a longer communication range to detect the distance between the wireless device and the biometric identification device; if the distance is detected to be gradually decreasing, activate the wireless communication module with a shorter communication range to detect the wireless device, and establish a communication connection with the detected wireless device as the wearable device.

[0011] Another aspect of this application provides a biometric-based data processing method applied to a wearable device. The method includes: establishing a communication connection with a biometric identification device; receiving a feedback instruction sent by the biometric identification device; the feedback instruction being generated by the biometric identification device capturing biometric information and performing optimized processing on the captured biometric information, based on posture data obtained during the biometric information capture process; and generating feedback information to characterize the participant's next posture based on the feedback instruction, so as to provide posture feedback based on the feedback information.

[0012] In another aspect of this application, a data processing device based on biometrics is provided, configured on a wearable device. The device includes: a connection module configured to establish a communication connection with a biometric identification device; a receiving module configured to receive a feedback instruction sent by the biometric identification device; the feedback instruction is generated by the biometric identification device capturing biometric information and performing optimized processing on the captured biometric information, based on posture data obtained during the biometric capture process; and a feedback module configured to generate feedback information characterizing the participant's next posture based on the feedback instruction, so as to provide posture feedback to the participant based on the feedback information.

[0013] In another exemplary embodiment, the feedback module is further configured to perform the following steps: obtain a preset feedback strategy; and execute the feedback information based on the feedback strategy to enable the participant to receive posture feedback.

[0014] In another exemplary embodiment, the feedback module is further configured to perform the following steps: when the feedback strategy setting entry on the function setting interface is detected to be triggered, the feedback strategy setting interface is displayed; feedback parameter items are set on the feedback strategy setting interface to generate the feedback strategy based on the set feedback parameter items.

[0015] In another exemplary embodiment, the feedback module is further configured to perform the following steps: selecting a feedback type item to be set; setting parameters for at least one feedback mode sub-item included in the feedback type item to generate the feedback strategy; each feedback mode sub-item includes a switch parameter and an intensity value parameter.

[0016] In another exemplary embodiment, the feedback module is further configured to perform the following steps: in response to an instruction to rename the feedback type item, displaying an item name selection interface containing at least two item names associated with the feedback type item; and renaming the feedback type item based on a selection operation triggered in the item name selection interface using the selected item name.

[0017] In another exemplary embodiment, the feedback module is further configured to perform the following steps: detect environmental information around the wearable device; adjust the parameters of the feedback strategy according to the environmental information, so as to execute the feedback information based on the adjusted feedback strategy.

[0018] Another aspect of this application provides an electronic device, including: one or more processors; and a memory for storing one or more computer programs, which, when executed by the one or more processors, cause the electronic device to implement the biometric-based data processing method as described above.

[0019] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor of an electronic device, causes the electronic device to perform the biometric-based data processing method as described above.

[0020] Another aspect of this application provides a computer program product, including a computer program that, when executed by a processor of an electronic device, implements the biometric-based data processing method described above.

[0021] In the technical solution provided in the embodiments of this application, a biometric identification device establishes a communication connection with a wearable device and generates a feedback command based on the posture data obtained during the capture and optimization of biometric information. By sending the feedback command to the wearable device, the wearable device provides posture feedback based on the feedback command. Since posture feedback does not require a biometric identification device, it does not create hardware dependence on the biometric identification device. Furthermore, the wearable device itself is an electronic device designed for users to perceive, transmit, and process information, enabling the collection, processing, feedback, and sharing of information anytime, anywhere. Therefore, user-oriented posture feedback can be achieved based on the existing hardware of the wearable device, making the biometric identification posture feedback solution provided in this application highly adaptable.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the implementation environment involved in this application;

[0024] Figure 2 This is a flowchart illustrating a biometric-based data processing method in an exemplary embodiment of this application;

[0025] Figure 3This is a flowchart of a biometric-based data processing method proposed in another exemplary embodiment of this application;

[0026] Figure 4 Based on Figure 3 The flowchart of another biometric-based data processing method proposed in the illustrated embodiment is shown.

[0027] Figure 5 This diagram illustrates an exemplary feedback policy settings interface;

[0028] Figure 6 This is an example diagram showing how a feedback strategy settings interface is navigated to.

[0029] Figure 7 This is a schematic diagram illustrating an exemplary application scenario shown in this application;

[0030] Figure 8 It indicates Figure 7 The flowchart of the palm gesture feedback process corresponding to the exemplary application scenario shown;

[0031] Figure 9 This is a block diagram illustrating a biometric-based data processing apparatus as shown in an exemplary embodiment of this application;

[0032] Figure 10 This is a block diagram illustrating a biometric-based data processing apparatus, as shown in another exemplary embodiment of this application.

[0033] Figure 11 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0034] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0035] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0036] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0037] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0038] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0039] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0040] Please refer to the following first. Figure 1 , Figure 1 This is a schematic diagram of the implementation environment involved in this application. The implementation environment is a biometric recognition posture reminder system, including a biometric recognition device 110, a wearable device 120, and a server 130.

[0041] The biometric identification device 110 establishes a wired or wireless communication connection with the server 130 beforehand. Based on this communication connection, the biometric identification device 110 transmits the captured biometric information to the server 130, enabling the server 130 to perform relevant processing on the biometric information, such as registration, verification, and selection, without limitation.

[0042] After detecting the communication signal from the wearable device 120, the biometric identification device 110 establishes a communication connection with the wearable device 120. Based on this communication connection, the biometric identification device 110 transmits feedback instructions generated from the posture data during the biometric selection process to the wearable device 120, causing the wearable device 120 to provide corresponding posture feedback to the user. This serves to remind the user that they need to adjust their biometric identification posture, thereby accelerating the success of biometric identification, or to inform the user that identification has been successful and the biometric identification process can be terminated.

[0043] It should be noted that this implementation environment does not restrict the specific type of biometric information. For example, the biometric identification device 110 can be non-contact biometric information such as facial recognition, palm prints, or iris scans. The term "non-contact" here refers to the fact that during biometric identification, the body part corresponding to the fingerprint information does not directly contact the biometric identification device 110. Similarly, this implementation environment does not restrict the specific type of wearable device 120. For example, wearable device 120 can be a smart bracelet, smartwatch, smart ring, smart glasses, etc.

[0044] Server 130 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services. This implementation environment does not impose any restrictions on this.

[0045] It should also be noted that this implementation environment can be adapted to different application scenarios based on varying application requirements, and no restrictions are imposed here. For example, in a mobile support scenario, the biometric identification device 110 is specifically implemented as a payment terminal. During mobile payment, the wearable device 120 provides the user with real-time gesture feedback, such as reminding the user of the correct payment posture or indicating the payment result. As another example, in a security access control system, the biometric identification device 110 is specifically implemented as an access control terminal. During user authentication, the wearable device 120 provides the user with real-time gesture feedback, such as reminding the user of the correct verification posture or indicating the verification result.

[0046] Please see Figure 2 , Figure 2 This is a flowchart illustrating a biometric-based data processing method as shown in an exemplary embodiment of this application. This method can be applied to... Figure 1The implementation environment shown can be specifically executed by the biometric identification device 110, or jointly executed by the biometric identification device 110 and the server 130. Of course, this method can also be applied to other implementation environments and executed by biometric identification devices in other implementation environments, or jointly executed by biometric identification devices and servers in other implementation environments. This embodiment does not impose any limitations.

[0047] like Figure 2 As shown, in an exemplary embodiment, the biometric data processing method includes steps S210-S230, which are described in detail below:

[0048] S210 detects wearable devices and establishes a communication connection with the detected wearable devices.

[0049] First, it should be noted that biometric identification devices are equipped with biometric sensors and wireless communication modules. The biometric sensors are used to capture the user's biological information, and the wireless communication module is used to transmit data with the wearable device. Biometric sensors include, for example, color cameras, infrared cameras, and depth cameras, etc., without limitation.

[0050] The detection of wearable devices by biometric identification devices refers to the process by which the biometric identification device detects wireless signals through a wireless communication module. If a gradually approaching wireless signal is detected, a communication connection is established with the wireless device that emitted the wireless signal, thereby enabling the biometric identification device to establish a communication connection with the wearable device.

[0051] In this embodiment, the wireless communication module can be an NFC (Near Field Communication), UWB (Ultra Wide Band) or Bluetooth module, etc., and there are no restrictions.

[0052] In short-range communication environments such as NFC, due to the short communication distance, biometric identification devices can usually accurately search for the wireless signals of wearable devices in practical application scenarios, and the communication connection between biometric identification devices and wearable devices usually does not fail.

[0053] However, in long-distance communication environments such as UWB and Bluetooth, biometric identification devices often detect numerous wireless signals, leading to the problem of the biometric identification device connecting to a different wearable device than the one being used by the user. To address this issue, as an exemplary implementation, considering that the user undergoing biometric identification is usually closest to the device, if the biometric identification device detects a wireless device approaching it via wireless communication detection, it begins searching for available wireless devices in the vicinity and establishes a communication connection with the nearest found wireless device. Therefore, even in scenarios with complex wireless signals, it can quickly and accurately establish a communication connection with the user's wearable device, thereby providing gesture feedback to the user during the biometric identification process via the wearable device.

[0054] In some embodiments, the wireless communication module can also use Wi-Fi Direct technology to enable communication between the biometric identification device and the wearable device. Wi-Fi Direct technology supports higher data transmission rates and longer communication distances, making it more suitable for scenarios requiring longer-distance communication or higher bandwidth, such as in large payment venues or complex access control systems.

[0055] In some embodiments, the wireless communication module may also employ an optical communication module, which transmits data via light waves to enable high-speed data transmission between the biometric identification device and the wearable device. Furthermore, since light waves cannot penetrate walls, the risk of data leakage is also lower, making it more suitable for scenarios requiring higher transmission rates or higher data transmission security.

[0056] In some embodiments, the biometric identification device and the wearable device may also be equipped with two or more wireless communication modules, such as a wireless communication module with a longer communication range and a wireless communication module with a shorter communication range, such as a UWB module and an NFC module. The UWB module is used to locate the distance between the wearable device and the biometric identification device, and the NFC module is used to transmit feedback commands.

[0057] It should be understood that the communication distance of a wireless communication module is relative. For example, the communication distance of a UWB module is greater than that of an NFC module; therefore, UWB modules are called longer-range communication modules, and NFC modules are called shorter-range communication modules. Similarly, the communication distance of a Wi-Fi Direct communication module is greater than that of a UWB module; therefore, Wi-Fi Direct modules are called longer-range communication modules, and UWB modules are called shorter-range communication modules.

[0058] The biometric identification device activates a longer-range wireless communication module to detect the distance between itself and the wireless device. If this distance gradually decreases, it activates a shorter-range wireless communication module to detect the wireless device and establish a communication connection with it, treating the detected wireless device as a wearable device. Thus, the combination of longer and shorter-range wireless communication methods further facilitates the accurate establishment of a communication connection between the biometric identification device and the wearable device.

[0059] S220, captures biological information and performs optimization processing on the captured biological information to obtain posture data during the biological information capture process.

[0060] Biometric identification devices capture a user's biological information through biometric sensors. By continuously capturing images containing biological information using these sensors, the device can effectively capture such information.

[0061] Optimization processing of captured biometric information refers to selecting one or more of the color images, depth images, and infrared images that meet the prerequisites for liveness detection and comparison recognition algorithms. For example, optimization processing is performed on images containing biometric information captured by a biometric sensor based on at least two preset image optimization conditions. These at least two image optimization conditions can be at least two of color image optimization, depth image optimization, and infrared image optimization, without specific limitations.

[0062] It is understandable that a color image can be obtained by an image sensor capturing light and converting it into an electrical signal, which is then processed. The process of optimizing a color image may include optimization based on the angle, size, centering, and clarity of the bio-information. This ensures that the optimized target image guarantees that the bio-information is facing the camera directly, avoiding excessive angular deviation. Furthermore, the bio-information should be of a suitable size in the image to accurately identify its features. The bio-information should be centered in the image to reduce recognition errors caused by positional shifts. Finally, the image should be clear, with visible details, moderate contrast, and no overexposure or underexposure.

[0063] Depth maps are used to represent the distance between human body parts containing biometric information and biometric sensors. Depth maps can also be acquired based on biometric sensors. The optimization process for depth maps can include optimization based on completeness and accuracy, so that the optimized depth map can completely cover the area of ​​biometric information, ensuring accurate acquisition of depth information. The depth information should accurately reflect the three-dimensional structure of the human body part, without noise or erroneous depth values.

[0064] Infrared images are generated by biometric sensors by capturing infrared radiation emitted by the human body in a scene. For example, an infrared sensor detects and converts infrared radiation into an electrical signal. The sensor's output electrical signal is then amplified, filtered, and digitally converted to enhance signal quality and facilitate subsequent image processing. The processed digital signal is then converted back into an infrared image. The optimization process for infrared images may include optimization based on brightness and contrast. The optimized infrared image should have moderate brightness, clearly displaying the biometric features without being too bright and losing detail. The contrast should also be high enough for the algorithm to accurately distinguish the biometric information from the background.

[0065] It should be noted that in scenarios where biometric information pertains to faces, color images, depth maps, and infrared images are typically used. Figure 3 The preferred method is to select the best biometric data. In scenarios where palm prints are used as biometric information, color images and infrared images are typically preferred.

[0066] By optimizing the captured biometric information, posture data can be obtained during the biometric capture process. This posture data characterizes the user's posture when using a biometric identification device for biometric recognition. For example, the offset distance of the biological entity relative to the biometric sensor can be determined based on the image processing data obtained during the optimization process. For instance, the offset distance can be determined based on data such as the angle, size, and centering of the biometric information obtained during color image optimization. Furthermore, the distance between the biological entity and the biometric sensor can be continuously acquired during the biometric capture process, thereby obtaining this distance and offset distance as posture data during the biometric capture process.

[0067] If a biometric identification device captures a target image that simultaneously meets at least two image selection criteria, the selection is considered successful, and biometric identification is then performed based on the biometric information contained in the target image. For example, the biometric identification device uploads the target image to a server, where biometric identification is performed on the target image, and the identified biometric information is registered and verified. Of course, in the case of successful selection, corresponding pose data can also be generated; for example, pose data can indicate successful selection.

[0068] S230 generates feedback commands based on posture data and sends the feedback commands to the wearable device, enabling the wearable device to provide posture feedback based on the feedback commands.

[0069] Biometric identification devices generate feedback commands based on posture data, which can remind users to adjust their posture to speed up the capture of effective biometric information. For example, in a palm-swiping scenario, if the posture data determines that the user's palm is too far away, the generated feedback command could be "bring your palm closer"; if it determines that the user's palm is too close, the generated feedback command could be "move your palm further away"; if it determines that the user's palm is shifted too far to the right, the generated feedback command could be "shift your palm to the left"; and if it determines that the user's palm is shifted too far to the left, the generated feedback command could be "shift your palm to the right".

[0070] The feedback instructions generated by the biometric identification device based on posture data can also be used to remind the user that the posture is correct or that the selection process has been successful, thus notifying the user that biometric identification has been successfully completed. In a palm-swiping scenario, if the posture data determines that the user's posture is correct, the generated feedback instruction could be "Keep the current palm-swiping posture unchanged." If the selection process is successful, the generated feedback instruction could be "Selection successful, please end palm swiping."

[0071] The biometric identification device sends feedback commands to the wearable device, enabling the wearable device to provide user-oriented posture feedback based on these commands. For example, by implementing at least one feedback mode for the user's vision, hearing, or touch on the wearable device, the user can immediately perceive whether the posture is correct during biometric identification. Detailed feedback methods are described in subsequent embodiments and will not be repeated here.

[0072] Therefore, the biometric gesture feedback scheme provided in this embodiment does not require a biometric device to provide gesture feedback, thus avoiding hardware dependence on such devices. The biometric device only needs to be equipped with a biometric sensor and a wireless communication module, without the need for additional hardware such as a display screen, lighting module, or speaker module. Wearable devices are electronic devices designed for users to sense, transmit, and process information, enabling the collection, processing, feedback, and sharing of information anytime, anywhere. Therefore, gesture feedback can be implemented based on the existing hardware of wearable devices, making the biometric gesture feedback scheme provided in this application highly adaptable.

[0073] Furthermore, users can personalize the way the wearable device provides gesture feedback when it receives feedback commands by operating the wearable device. This makes the biometric gesture feedback solution provided in this application more flexible and adaptable to different scenarios. For example, the feedback method can be set to vibration reminder in quiet scenarios, and to loud audio reminder in noisy scenarios. Examples are not listed here.

[0074] Moreover, the communication connection between the wearable device and the biometric identification device is established automatically without requiring manual operation by the user, which makes the biometric identification posture feedback scheme provided in this application easier to implement.

[0075] Please see Figure 3 , Figure 3 This is a flowchart of a biometric-based data processing method proposed in another exemplary embodiment of this application. This method can be applied to... Figure 1 The implementation environment shown can be specifically executed by wearable device 120, for example. This method can also be applied to other implementation environments and executed by wearable devices in other implementation environments, and this embodiment does not impose any limitations.

[0076] like Figure 3 As shown, in an exemplary embodiment, the biometric data processing method includes steps S310-S330, which are described in detail below:

[0077] S310 establishes a communication connection with biometric identification devices.

[0078] For a wearable device to provide feedback on a user's biometric gestures, it needs to obtain feedback commands from the biometric identification device. Therefore, the wearable device needs to establish a communication connection with the biometric identification device. Similar to the previous embodiments, this embodiment does not restrict the method by which the wearable device establishes a communication connection with the biometric identification device.

[0079] S320, receiving feedback instructions sent by the biometric identification device; the feedback instructions are generated by the biometric identification device capturing biometric information and performing optimal processing on the captured biometric information, based on the posture data obtained during the biometric information capture process.

[0080] Please refer to the description of the foregoing embodiments for the method of generating feedback instructions; this embodiment will not repeat it here.

[0081] S330, based on the feedback instruction, generates feedback information to characterize the participant's next posture, and provides posture feedback based on this feedback information.

[0082] It is understood that the participants mentioned in this embodiment refer to users who use biometric identification devices for biometric identification.

[0083] After receiving feedback instructions from a biometric identification device, the wearable device generates feedback information representing the participant's next posture. This feedback information provides posture feedback to the participant, allowing them to receive reminders about their next move based on the posture feedback from the wearable device. For example, the feedback information can be displayed on the wearable device's screen, played through its speaker, or used to alert the user via vibration, among other methods.

[0084] In summary, based on the hardware inherent in wearable devices, users can receive prompts regarding their next posture without relying on biometric identification devices. These devices only require basic hardware such as biometric sensors and wireless communication modules, making the biometric posture feedback scheme provided in this embodiment highly applicable. Users can quickly adjust their posture based on the prompts from the wearable device, allowing the biometric identification device to capture valid biometric information, thus accelerating the success of biometric identification. Users can also track the progress of biometric identification, improving the user experience and making biometric technology easier to popularize and apply.

[0085] Please continue reading. Figure 4 In one exemplary embodiment, the wearable device generates feedback information characterizing the participant's next posture based on feedback instructions. The process of providing posture feedback based on this feedback information includes the following steps:

[0086] S410, obtain the preset feedback strategy;

[0087] S420 executes feedback information based on a feedback strategy to provide participants with postural feedback.

[0088] In the above process, the wearable device has a preset feedback strategy, which is used to constrain how the wearable device provides gesture feedback based on the feedback instructions.

[0089] As an exemplary implementation, when the wearable device detects that the feedback strategy setting entry on the function settings interface has been triggered, it displays the feedback strategy setting interface and generates a feedback strategy based on the set feedback parameter items by setting the feedback parameter items on the feedback strategy setting interface. For example... Figure 5 As shown, Figure 5 This is a schematic diagram of an exemplary feedback strategy settings interface. The exemplary feedback strategy settings interface contains three feedback parameter items: "screen feedback", "sound feedback" and "vibration feedback". Each feedback parameter item can be independently set with on / off parameters and intensity values.

[0090] Therefore, by customizing feedback parameters through the feedback strategy settings interface provided by the wearable device, users can enable the device to obtain personalized feedback strategies. Wearable devices provide gesture feedback to users based on these personalized strategies, making biometric gesture feedback solutions highly flexible and adaptable to various scenarios.

[0091] As another exemplary implementation, the feedback strategy setting interface provides at least two feedback type items. The wearable device, based on the user's trigger operation, selects the feedback type item to be set and sets parameters for at least one feedback method sub-item included in the selected feedback type item, thus obtaining the set feedback strategy. For example... Figure 6 As shown, Figure 6 This is a schematic diagram illustrating the display of an exemplary feedback strategy settings interface. The interface provides three feedback types: "Feedback Type 1," "Feedback Type 2," and "Feedback Type 3." When a user selects "Feedback Type 1" as the desired feedback type, the interface further displays three sub-items within "Feedback Type 1": "Screen Feedback," "Sound Feedback," and "Vibration Feedback." Each sub-item includes on / off parameters and intensity values. Users can individually configure the on / off parameters and intensity values ​​for each sub-item to achieve a personalized feedback strategy.

[0092] In some embodiments, the feedback strategy settings interface also supports renaming feedback type items, allowing users to modify the name of each feedback type item. For example, the feedback strategy settings interface allows users to manually enter item names, and it can also support selecting the name of each feedback type item. For instance, when a user triggers the entry to modify the name of a feedback type item, the feedback strategy settings interface displays at least two item names pre-associated with that feedback type item. These associated item names may correspond to different application scenarios or the type of feedback instruction, allowing the modification of the item name based on the selected item name triggered by the user.

[0093] For example, when the pre-associated item names for feedback type items correspond to different application scenarios, users can change "Feedback Type 1" to "Mute Feedback," "Feedback Type 2" to "Regular Feedback," and "Feedback Type 3" to "Increase Volume Feedback," etc. When the pre-associated item names for feedback type items correspond to feedback instructions, users can change "Feedback Type 1" to "Continuous Monitoring Feedback" and "Feedback Type 2" to "Optimized Success Feedback."

[0094] Therefore, by configuring the feedback type, users can tailor the feedback strategy to provide personalized gesture feedback, further enhancing the personalization of the feedback strategy. For example, in a scenario where a user is studying in a library, selecting "silent feedback" as the default feedback strategy for the wearable device will only provide vibration feedback to remind the user of biometric recognition posture; in a normal scenario, selecting "normal feedback" as the default feedback strategy for the wearable device will provide biometric recognition posture reminders through screen, sound, and vibration.

[0095] For example, if the feedback instruction indicates that the user needs to adjust their posture for further biometric identification, "continuous detection feedback" will be used as the feedback strategy to provide posture feedback to the user through the screen and sound; if the feedback instruction indicates that the selection is successful, "selection success feedback" will be used as the feedback strategy to provide posture feedback to the user through the screen, sound and vibration.

[0096] In other exemplary embodiments, when the wearable device executes feedback information based on a preset feedback strategy, it also detects surrounding environmental information, thereby adjusting the parameters of the feedback strategy according to the detected environmental information, so as to execute the feedback information based on the adjusted feedback strategy. For example, if the detected environmental information indicates that the surrounding environment is very quiet, the feedback strategy is adjusted to enable the feedback parameters "vibration feedback" and "screen feedback" and disable the feedback parameter "sound feedback", or the feedback strategy is adjusted to enable the feedback type "silent feedback". Thus, by automatically adjusting the feedback strategy by detecting the environmental information around the wearable device, the flexibility and scene adaptability of the biometric recognition posture feedback scheme are further enhanced.

[0097] To better understand the biometric identification feedback scheme proposed in the embodiments of this application, the following exemplary application scenarios will be used to illustrate the biometric identification feedback scheme proposed in the embodiments of this application.

[0098] Please see Figure 7 and Figure 8 , Figure 7 This is a schematic diagram illustrating an exemplary application scenario shown in this application. Figure 8 It shows Figure 7 The flowchart shown illustrates the palm-swiping gesture feedback process for an exemplary application scenario. This application scenario specifically involves palm-swiping payment; the biometric identification device is implemented as a palm-swiping terminal, and the wearable device is implemented as a smart bracelet.

[0099] The palm-swiping terminal includes a biometric sensor for capturing the user's palm print. For example, the biometric sensor may include a color camera and an infrared camera; the color camera captures a color image, and the infrared camera captures an infrared image. The palm-swiping terminal is also equipped with a wireless communication module, such as an NFC, UWB, or Bluetooth module, for data transmission with wearable devices.

[0100] The smart bracelet is equipped with a visual display unit, such as an LED (Light Emitting Diode) screen or a small screen, an audio output unit, such as a speaker, and a haptic feedback unit, such as a vibration motor. Of course, in some embodiments, including at least one of these modules—visual display unit, audio output unit, and haptic feedback unit—is sufficient to provide perceptible feedback to the user. The smart bracelet also includes a wireless communication module for pairing with and receiving data from a palm-swipe terminal.

[0101] The smart bracelet allows users to personalize parameters such as the feedback method and intensity of palm-swiping gestures. For different feedback commands, the smart bracelet can provide different responses based on these personalized settings. For example, when it detects continuous palm swiping, it can provide feedback to the user via the screen and sound, such as moving the palm closer; when it detects a successful selection, it can provide feedback to the user via the screen, sound, and vibration, such as removing the palm.

[0102] When a user brings their palm, which is wearing a smart bracelet, close to the palm-swiping terminal to perform a palm-swiping operation, the palm-swiping terminal and the smart bracelet detect each other and establish a wireless communication connection. Both the palm-swiping terminal and the wearable device are equipped with wireless modules such as UWB, NFC, and Bluetooth, keeping these modules enabled and continuously detecting wireless devices in the firmware. For example, the location of the wireless device can be determined through the UWB module, and feedback commands can be transmitted through the NFC module.

[0103] When a user extends their palm and places it above the camera on the palm-swiping terminal, the camera detects the palm and begins the optimization phase, continuously detecting the user's palm posture data, including the distance between the palm and the camera and the distance the palm deviates from the camera, and sending feedback commands to the smart bracelet in real time via the wireless communication module.

[0104] The smart bracelet receives feedback commands from the palm-swiping terminal and responds accordingly. For example... Figure 7As shown, when the feedback command instructs the user to bring their palm closer, the smart bracelet prompts the user to adjust their palm position using both screen and sound feedback. After the user adjusts their palm position, the feedback command indicates that the current palm position is correct, and the smart bracelet reminds the user that the palm position is correct. Once the user maintains the correct palm position and the palm-swiping terminal successfully selects the correct position, the feedback command indicates that the selection is successful, and the smart bracelet reminds the user of the successful selection through screen, sound, and vibration feedback. After the user perceives the successful palm-swiping selection based on the smart bracelet's reminder, they actively remove their palm. The palm-swiping terminal detects that the palm and the wearable device have moved away and disconnects the communication connection with the wearable device.

[0105] Therefore, the embodiments of this application, combining the real-time communication and feedback mechanism between the palm-swiping terminal and the wearable device, can bring significant technical improvements and enhanced user experience. Specifically, based on the visual, auditory, and tactile feedback provided by the wearable device, it ensures that users can receive instant palm-swiping posture feedback in any environment, even in noisy or limited-view environments; users can choose a suitable feedback method according to their personal needs or environment, such as selecting a silent mode in a quiet environment to receive only vibration feedback; users do not need to manually set up the connection between the wearable device and the palm-swiping terminal, but the wearable device automatically connects to the palm-swiping terminal when it approaches the palm-swiping device, saving users from complex setup operations and making the palm-swiping application easier to promote; and, regardless of the environment, users can receive appropriate feedback reminders through the wearable device, ensuring the success rate and security of user operations.

[0106] The significant technological improvements and enhanced user experience described above also increase ease of operation and system security. These enhancements make palm recognition technology more practical and widely applicable, especially in real-world applications with diverse needs and complex conditions.

[0107] It should be noted that the technical solutions proposed in the embodiments of this application can also be applied to other application scenarios. For example, the biometric identification device is specifically implemented as a face-scanning terminal, and the wearable device is specifically implemented as smart glasses. Smart glasses, utilizing their display function, can project feedback information directly into the user's field of vision, allowing feedback information to be displayed directly in the user's field of vision, such as "move the face closer," "move the face slightly to the left," or "selection successful, please leave." Additionally, a smart bracelet can be replaced with a smart ring. A smart ring typically includes a miniature vibration motor, LED indicators, and a miniature speaker, providing the same visual, auditory, and tactile feedback as a smart bracelet.

[0108] Therefore, the embodiments of this application can not only adapt to a wider range of application scenarios, but also provide more diverse user choices, thereby increasing the market's attractiveness and competitiveness.

[0109] Please see Figure 9 , Figure 9 This is a block diagram illustrating a biometric-based data processing apparatus according to an exemplary embodiment of this application. The apparatus can be applied to… Figure 1 The implementation environment shown can be configured, for example, on the biometric identification device 110, or jointly on the biometric identification device 110 and the server 130. Of course, this device can also be applied to other implementation environments and configured on biometric identification devices in other implementation environments, or jointly configured on biometric identification devices and servers in other implementation environments. This embodiment does not impose any limitations.

[0110] like Figure 9 As shown, the biometric data processing device includes:

[0111] The detection module 910 is configured to detect wearable devices and establish a communication connection with the detected wearable devices;

[0112] The optimization module 920 is configured to capture biological information and perform optimization processing on the captured biological information to obtain posture data during the biological information capture process.

[0113] The processing module 930 is configured to generate feedback instructions based on posture data and send the feedback instructions to the wearable device, so that the wearable device can provide posture feedback to the participant based on the feedback instructions.

[0114] In another exemplary embodiment, the preferred module 920 is further configured to perform the following steps:

[0115] The system continuously captures images containing biological information using a biometric sensor and performs optimization on the images containing biological information based on at least two preset image optimization conditions.

[0116] The distance between the biological body and the biometric sensor is continuously acquired, and the offset distance of the biological body relative to the biometric sensor is determined based on the image processing data obtained during the optimization process, so as to acquire the distance and offset distance as posture data in the process of capturing biological information.

[0117] In another exemplary embodiment, the preferred module 920 is further configured to perform the following steps:

[0118] If a target image that simultaneously meets at least two image selection criteria is captured, the selection is considered successful, and biometric identification is performed based on the biometric information contained in the target image.

[0119] In another exemplary embodiment, the detection module 910 is further configured to perform the following steps:

[0120] If wireless communication detection detects that a wireless device is near the biometric identification device, then the search for available wireless devices around the biometric identification device will begin.

[0121] Establish a communication connection with the nearest wireless device detected.

[0122] In another exemplary embodiment, both the biometric identification device and the wearable device are provided with a wireless communication module with a longer communication range and a wireless communication module with a shorter communication range; the detection module 910 is further configured to perform the following steps:

[0123] Activate a wireless communication module with a longer communication range to detect the distance between the wireless device and the biometric identification device;

[0124] If the distance is detected to be gradually decreasing, the wireless communication module with a shorter communication range is activated to detect wireless devices, and the detected wireless devices are treated as wearable devices to establish a communication connection with the wearable devices.

[0125] Please see Figure 10 , Figure 10 This is a block diagram illustrating a biometric-based data processing apparatus according to another exemplary embodiment of this application. The apparatus can be applied to… Figure 1 The implementation environment shown can be configured, for example, on wearable device 120. Of course, the device can also be applied to other implementation environments and configured on wearable devices in other implementation environments; this embodiment is not limiting.

[0126] like Figure 10 As shown, the biometric data processing device includes:

[0127] The connection module 1010 is configured to establish a communication connection with the biometric identification device;

[0128] The receiving module 1020 is configured to receive feedback instructions sent by the biometric identification device; the feedback instructions are generated by the biometric identification device capturing biometric information and performing optimal processing on the captured biometric information, based on the posture data obtained during the biometric capture process.

[0129] Feedback module 1030 is configured to generate feedback information to characterize the next posture of the participant based on feedback instructions, so as to provide posture feedback to the participant based on the feedback information.

[0130] In another exemplary embodiment, the feedback module 1030 is further configured to perform the following steps:

[0131] Obtain the preset feedback strategy;

[0132] Feedback information is provided based on a feedback strategy to enable participants to receive gestural feedback.

[0133] In another exemplary embodiment, the feedback module 1030 is further configured to perform the following steps:

[0134] When the feedback strategy settings entry on the function settings interface is triggered, the feedback strategy settings interface is displayed.

[0135] Configure the feedback parameters on the feedback strategy settings interface to generate a feedback strategy based on the configured parameters.

[0136] In another exemplary embodiment, the feedback module 1030 is further configured to perform the following steps:

[0137] Select the feedback type to be set;

[0138] Parameter settings are applied to at least one feedback mode sub-item within the feedback type item to generate a feedback strategy; each feedback mode sub-item includes a switch parameter and an intensity value parameter.

[0139] In another exemplary embodiment, the feedback module 1030 is further configured to perform the following steps:

[0140] In response to an instruction that triggers the renaming of a feedback type item, an item name selection interface is displayed, which contains at least two item names associated with the feedback type item.

[0141] Based on the selection operation triggered in the item name selection interface, the selected item name will be renamed to the feedback type item.

[0142] In another exemplary embodiment, the feedback module 1030 is further configured to perform the following steps:

[0143] Detect environmental information around wearable devices;

[0144] The parameters of the feedback strategy are adjusted based on environmental information, and feedback information is executed based on the adjusted feedback strategy.

[0145] It should be noted that the apparatus and method provided in the above embodiments belong to the same concept, and the specific manner in which each module and unit performs its operation has been described in detail in the method embodiments, and will not be repeated here. In practical applications, the biometric data processing apparatus provided in the above embodiments can be configured to perform the above functions by different functional modules as needed, that is, the internal structure of the apparatus can be divided into different functional modules to complete all or part of the functions described above, and this is not a limitation here.

[0146] Embodiments of this application also provide an electronic device, including: one or more processors; and a memory for storing one or more computer programs, which, when executed by the one or more processors, cause the electronic device to implement the biometric data processing method provided in the above embodiments.

[0147] Figure 11 A schematic diagram of a computer system suitable for implementing an electronic device according to embodiments of this application is shown. It should be noted that the electronic device can be... Figure 1 The biometric identification device 110 or wearable device 120 in the illustrated implementation environment can also be a biometric identification device or wearable device in other implementation environments; no limitation is imposed here. It should also be noted that... Figure 11 The computer system 1100 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0148] like Figure 11 As shown, the computer system 1100 includes a Central Processing Unit (CPU) 1101, which can perform various appropriate actions and processes based on a computer program stored in Read-Only Memory (ROM) 1102 or a computer program loaded from storage portion 1108 into Random Access Memory (RAM) 1103, such as performing the methods described in the above embodiments. Various computer programs and data required for system operation are also stored in RAM 1103. The CPU 1101, ROM 1102, and RAM 1103 are interconnected via bus 1104. An Input / Output (I / O) interface 1105 is also connected to bus 1104.

[0149] The following components are connected to I / O interface 1105: an input section 1106 including a keyboard, mouse, etc.; an output section 1107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to I / O interface 1105 as needed. Removable media 1111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1110 as needed so that computer programs read from them can be installed into storage section 1108 as needed.

[0150] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1109, and / or installed from removable medium 1111. When the computer program is executed by central processing unit (CPU) 1101, it performs various functions defined in the system of this application.

[0151] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0152] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0153] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0154] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor of an electronic device, implements the biometric-based data processing method described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not incorporated into the electronic device.

[0155] Another aspect of this application provides a computer program product comprising a computer program stored in a computer-readable storage medium. A processor of an electronic device reads the computer program from the computer-readable storage medium and executes the computer program, causing the electronic device to perform the biometric-based data processing methods provided in the various embodiments described above.

[0156] The above description is merely a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.

[0157] It is understood that in the specific embodiments of this application, data related to signal detection of wireless devices and capture of biological information are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

Claims

1. A data processing method based on biometrics, characterized in that, The method, applied to biometric identification devices, includes: Detect wearable devices and establish communication connections with the detected wearable devices; Biological information is captured, and based on the optimized processing of the captured biological information, posture data is obtained during the biological information capture process. A feedback instruction is generated based on the posture data, and the feedback instruction is sent to the wearable device, so that the wearable device can provide posture feedback based on the feedback instruction.

2. The method according to claim 1, characterized in that, The process of capturing biological information and, based on the optimized processing of the captured biological information, obtaining posture data during the biological information capture process includes: The biometric sensor is enabled to continuously capture images containing biological information, and the images containing biological information are optimized based on at least two preset image optimization conditions; The distance between the biological body and the biometric sensor is continuously acquired, and the offset distance of the biological body relative to the biometric sensor is determined based on the image processing data obtained during the optimization process, so as to use the distance between the biological body and the offset distance as posture data in the process of capturing biological information.

3. The method according to claim 2, characterized in that, The method further includes: If a target image that simultaneously satisfies at least two of the preferred image conditions is captured, the selection is determined to be successful, and biometric identification is performed based on the biometric information contained in the target image.

4. The method according to claim 1, characterized in that, The process of detecting wearable devices and establishing a communication connection with the detected wearable devices includes: If, based on wireless communication detection, a wireless device is detected gradually approaching the biometric identification device, then the search for available wireless devices around the biometric identification device begins. The nearest wireless device is identified as the wearable device, and a communication connection is established with the wireless device.

5. A data processing method based on biometrics, characterized in that, Applied to wearable devices, the method includes: Establish a communication connection with biometric identification devices; The device receives a feedback instruction sent by the biometric identification device. The feedback instruction is generated by the biometric identification device capturing biometric information and performing optimal processing on the captured biometric information, based on the posture data obtained during the biometric capture process. Based on the feedback instruction, feedback information is generated to characterize the participant's next posture, so as to provide posture feedback based on the feedback information.

6. The method according to claim 5, characterized in that, The step of generating feedback information to characterize the participant's next posture based on the feedback instruction, and providing posture feedback based on the feedback information, includes: Obtain the preset feedback strategy; The feedback information is executed based on the feedback strategy to provide the participant with gesture feedback.

7. The method according to claim 6, characterized in that, The method further includes: When the feedback strategy settings entry on the function settings interface is triggered, the feedback strategy settings interface is displayed. On the feedback strategy setting interface, the feedback parameter items are set to generate the feedback strategy based on the set feedback parameter items.

8. The method according to claim 7, characterized in that, The step of setting feedback parameters on the feedback strategy setting interface to generate the feedback strategy based on the set feedback parameters includes: Determine the feedback type item to be set; The feedback strategy is generated by setting parameters for at least one feedback method sub-item included in the feedback type item; each feedback method sub-item includes a switch parameter and an intensity value parameter.

9. The method according to claim 6, characterized in that, The execution of the feedback information based on a preset feedback strategy to provide the participant with posture feedback includes: Detect environmental information around the wearable device; The parameters of the feedback strategy are adjusted based on the environmental information, so that the feedback information is executed based on the adjusted feedback strategy.

10. A data processing device based on biometrics, characterized in that, The device, configured on a biometric identification device, includes: The detection module is configured to detect wearable devices and establish a communication connection with the detected wearable devices. The optimization module is configured to capture biological information and perform optimization processing on the captured biological information to obtain posture data during the biological information capture process. The processing module is configured to generate a feedback instruction based on the posture data and send the feedback instruction to the wearable device, so that the wearable device can provide posture feedback based on the feedback instruction.

11. A data processing device based on biometrics, characterized in that, Configured on a wearable device, the device includes: A connection module configured to establish a communication connection with a biometric identification device; The receiving module is configured to receive feedback instructions sent by the biometric identification device; the feedback instructions are generated by the biometric identification device capturing biometric information and performing optimal processing on the captured biometric information, based on the posture data obtained during the biometric capture process. The feedback module is configured to generate feedback information representing the participant's next posture based on the feedback instruction, so as to provide posture feedback based on the feedback information.

12. An electronic device, characterized in that, include: One or more processors; A memory for storing one or more computer programs that, when executed by one or more processors, cause the electronic device to perform the method as described in any one of claims 1-9.

13. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the processor of the electronic device, causes the electronic device to perform the method of any one of claims 1-9.

14. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor of the electronic device, it implements the method as described in any one of claims 1-9.

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