A mobile phone screen unlocking method based on gesture recognition

Abstract

The application relates to the technical field of mobile phone screen unlocking, and relates to a mobile phone screen unlocking method based on gesture recognition. The method comprises the following steps: S1, touch detection is performed on a mobile phone screen, so that a real-time touch pattern and a real-time completion time are generated; and S2, standard touch patterns and hand image data of set gesture types are collected. The application adjusts a time threshold value according to a formula by counting the average completion time of historical gesture operations of a user, so that the time threshold value is automatically lengthened if the actual operation time of the user is generally longer than the standard completion time, and the time threshold value is shortened if the actual operation time is shorter, the adjustment of the time threshold value avoids misjudgment caused by slow user operation speed, the unlocking is more in line with the actual operation rhythm of the user, and meanwhile, the basic deviation threshold value is adjusted according to the real-time completion time in the gesture comparison link, the adjustment avoids unlocking obstruction caused by temporary operation state fluctuation of the user, guarantees the rationality of judgment, and greatly improves operation convenience.

Description

Technical Field

[0001] This invention relates to the field of mobile phone screen unlocking technology, and more specifically, to a mobile phone screen unlocking method based on gesture recognition. Background Technology

[0002] In the field of mobile smart device technology, gesture recognition, as a convenient human-computer interaction method, is widely used in mobile phone screen unlocking scenarios. Its core purpose is to provide users with fast and intuitive unlocking operations while ensuring device information security, replacing the cumbersome operation or hardware dependence problems of traditional password, fingerprint and other unlocking methods.

[0003] Existing gesture recognition unlocking technologies primarily operate in scenarios where users unlock their phones daily, including high-frequency daily use unlocking (such as unlocking before answering calls or viewing messages) and low-frequency unlocking after prolonged screen locking. However, existing technologies often employ fixed judgment criteria, such as preset fixed trajectory deviation thresholds and completion time thresholds, which cannot adapt to the different operating habits of various users. Users with slower operating speeds are prone to unlocking failures because the gesture completion time exceeds the fixed threshold, while users with lower finger dexterity may be misjudged as having engaged in illegal operations due to slightly exceeding the standard trajectory deviation. This reduces the convenience of unlocking and affects the user experience, contradicting the core purpose of convenient unlocking. Therefore, a gesture recognition-based mobile phone screen unlocking method is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a mobile phone screen unlocking method based on gesture recognition to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, a method for unlocking a mobile phone screen based on gesture recognition is provided, comprising the following steps:

[0006] S1. Perform touch detection on the mobile phone screen to generate real-time touch graphics and real-time completion time;

[0007] S2. Collect standard touch graphics and hand image data saved for the set gesture type, and collect historical touch graphics, and classify the historical touch graphics in the database according to the gesture type.

[0008] S3. Segment the finger regions based on the hand image data, and perform basic deviation threshold analysis on the segmented finger regions. At the same time, perform maximum deviation threshold analysis by combining the standard touch pattern with the historical touch pattern.

[0009] S4. Extract the standard completion time of the standard touch pattern, and at the same time analyze the average completion time of the historical touch patterns corresponding to the gesture type. Then, set the time threshold based on the average completion time and the standard completion time.

[0010] S5. Compare the real-time completion time with the time threshold, retain the real-time touch graphic based on the comparison result, analyze the deviation value of the real-time touch graphic with the standard touch graphic, and select the basic deviation threshold corresponding to the standard touch graphic with the smallest deviation value for adjustment in combination with the real-time completion time.

[0011] S6. Compare the deviation value with the basic deviation threshold adjusted in S5 and the maximum deviation threshold. If the deviation value is greater than the basic deviation threshold but less than the maximum deviation threshold, other gesture types are required for verification.

[0012] As a further improvement to this technical solution, the mobile phone screen in S1 is a touch screen, and the touch is recorded by capacitive sensing. At the same time, for the historical touch patterns collected in S2, only the historical touch patterns that have been verified are retained.

[0013] As a further improvement to this technical solution, S1 includes performing touch detection on the mobile phone screen, recording touch data when the mobile phone screen is detected to be touched, then capturing the recorded touch data in real time as a touch graphic, and obtaining the real-time completion time of the touch data.

[0014] As a further improvement to this technical solution, step S2 is as follows:

[0015] S2.1 Collect the standard touch graphics saved by the user when setting the gesture type, and collect the user's hand image data after setting the gesture type;

[0016] S2.2 Collect historical touch images and clean them up regularly. Then, filter the historical touch images by gesture type and classify and save the filtered historical touch images in the database.

[0017] As a further improvement to this technical solution, step S3 is as follows:

[0018] S3.1. Perform finger region segmentation based on hand image data, obtain the finger region corresponding to each finger in the hand image data, then compare the deviations of the finger regions of each finger with each other, and select the maximum deviation value as the basic deviation threshold from the comparison results.

[0019] S3.2. Compare the standard touch pattern with the historical touch pattern to determine the deviation, and select the maximum deviation value as the maximum deviation threshold from the comparison results.

[0020] As a further improvement to this technical solution, step S4 is as follows:

[0021] S4.1 Extract the completion time of standard touch graphics and extract the completion time of historical touch graphics. Then, summarize the completion times of historical touch graphics corresponding to the gesture type and perform average completion time analysis to obtain the average completion time of historical touch graphics corresponding to the gesture type.

[0022] S4.2 Set the standard completion time as the initial time threshold, and then adjust the initial time threshold by using the average completion time;

[0023] If the average completion time is greater than the standard completion time, the time threshold will be adjusted by extending the time.

[0024] If the average completion time is less than the standard completion time, the time threshold will be adjusted by shortening the time.

[0025] As a further improvement to this technical solution, step S5 is as follows:

[0026] S5.1. The real-time completion time is compared with a time threshold. If the real-time completion time exceeds the time threshold, the real-time touch graphic is deemed invalid and not retained. Conversely, if the real-time completion time does not exceed the time threshold, the real-time touch graphic is deemed retained.

[0027] S5.2. Combine the retained real-time touch graphics with all standard touch graphics to perform deviation value analysis and obtain the deviation value between the real-time touch graphics and all standard touch graphics.

[0028] S5.3. Select the standard touch pattern with the smallest deviation value from the list of deviation values ​​obtained in S5.2, and then adjust the basic threshold corresponding to the standard touch pattern in combination with the real-time completion time.

[0029] The shorter the real-time completion time, the larger the basic deviation threshold will be.

[0030] The longer the real-time completion time, the smaller the base deviation threshold will be.

[0031] As a further improvement to this technical solution, step S6 is as follows:

[0032] S6.1 Compare the minimum deviation value with the basic deviation threshold and the maximum deviation threshold of the corresponding standard touch pattern;

[0033] S6.2. If the deviation value is less than the basic deviation threshold, the phone screen is determined to be unlocked successfully.

[0034] S6.3 When the deviation value is greater than the basic deviation threshold but less than the maximum deviation threshold, other gesture types are required for verification. If the other gesture types are drawn successfully, the phone screen is determined to be unlocked successfully.

[0035] S6.4 If the deviation value is greater than the maximum deviation threshold, the phone screen unlocking is determined to have failed.

[0036] As a further improvement to this technical solution, S6.2 requires other gesture types to be drawn for verification. If there is only one gesture type, then only the basic deviation threshold is used for comparison.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] 1. In this gesture recognition-based mobile phone screen unlocking method, by statistically analyzing the average completion time of the user's historical gesture operations, if the user's actual operation time is generally longer than the standard completion time, the time threshold will be automatically extended; if the actual time is shorter, the time threshold will be shortened. The time threshold is adjusted according to a formula to avoid misjudgment due to slow user operation speed, making unlocking more in line with the user's actual operation rhythm. At the same time, in the gesture comparison stage, the basic deviation threshold is adjusted in combination with the real-time completion time. This adjustment not only avoids unlocking obstacles caused by fluctuations in the user's temporary operation state, but also ensures the rationality of the judgment, greatly improving the convenience of operation.

[0039] 2. In this gesture recognition-based mobile phone screen unlocking method, the maximum deviation of the finger area is obtained by hand image segmentation as the basic deviation threshold, establishing the basic standard for gesture shape matching. The maximum deviation threshold is set by combining the maximum deviation between the standard touch pattern and the historical touch pattern, forming a dual protection of basic standard and upper limit control. When an unauthorized user imitates the gesture, their operation deviation is very likely to exceed the maximum deviation threshold, directly determining that the unlocking fails. In addition, the time threshold is combined to filter obviously abnormal operations, which greatly reduces the risk of a single gesture being cracked. Even if an unauthorized user manages to pass the comparison once, it is difficult to pass subsequent verification, further strengthening the mobile phone information security.

[0040] 3. In this gesture recognition-based mobile phone screen unlocking method, the collected historical touch patterns are periodically cleaned up, invalid data is removed, and the data is categorized and stored in the database according to gesture type. This process not only reduces the storage burden on the mobile phone and avoids system lag caused by data redundancy, but also ensures the quality of historical data used for analysis. It provides accurate data support for average completion time calculation, maximum deviation threshold analysis, etc. Based on continuously updated historical data, the judgment criteria such as time threshold and deviation threshold are continuously optimized. As the user's usage time increases, the system's understanding of the user's operating habits becomes more accurate, and the judgment criteria will be dynamically adjusted accordingly, always maintaining the best balance between security and ease of use. Attached Figure Description

[0041] Figure 1 This is an overall flowchart of the present invention;

[0042] Figure 2 This is a flowchart illustrating the process of collecting hand image data from a user according to the present invention.

[0043] Figure 3 This is a flowchart illustrating the finger region segmentation process based on hand image data according to the present invention.

[0044] Figure 4 This is a flowchart illustrating the process of extracting the completion time of a standard touch graphic according to the present invention.

[0045] Figure 5 The flowchart for obtaining the deviation value between the real-time touch graphic and all standard touch graphics in this invention is shown below.

[0046] Figure 6 This is a flowchart illustrating how the present invention compares the minimum deviation value with the basic deviation threshold and the maximum deviation threshold of the corresponding standard touch graphic. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please see Figure 1 - Figure 6 As shown, the purpose of this embodiment is to provide a method for unlocking a mobile phone screen based on gesture recognition, including the following steps:

[0049] S1. Perform touch detection on the phone screen to generate real-time touch graphics and real-time completion time; as the input source for the entire unlocking process, obtain raw user gesture data;

[0050] In S1, the phone screen is a touch screen, and touch is recorded through capacitive sensing. The phone's touch screen is in a real-time detection state. When a touch operation is detected on the screen, the subsequent data recording process is started.

[0051] Meanwhile, for the historical touch images collected in S2, only the verified historical touch images are retained. The selection is based on whether or not they are verified, and only the verified historical touch images are retained for subsequent related analysis and comparison.

[0052] S1 includes touch detection on the mobile phone screen. When the touch on the mobile phone screen is detected, the touch data is recorded. Then, the recorded touch data is captured in real time as a touch graphic, and the real-time completion time of the touch data is obtained.

[0053] Based on the recorded touch data, it is transformed into intuitive real-time touch graphics, that is, the trajectory graphic formed by the user's touch operation on the screen. While recording the touch data, the time interval from the start to the end of the touch is accurately obtained, i.e., the real-time completion time.

[0054] S2. Collect standard touch patterns and hand image data saved for the set gesture types, and collect historical touch patterns. Combine the historical touch patterns with the gesture types for database classification. Construct a standard dataset for comparison and a historical dataset for optimization to provide a data foundation for subsequent threshold analysis.

[0055] The steps for S2 are as follows:

[0056] S2.1 Collect the standard touch graphics saved by the user when setting the gesture type, and collect the user's hand image data after setting the gesture type;

[0057] The system collects standard touch graphics corresponding to various gesture types pre-defined by the user. These graphics serve as benchmark templates for subsequent comparisons. After the user completes the gesture type setting, the system guides the user to provide their hand image data, providing basic information for subsequent finger area analysis.

[0058] S2.2 Collect historical touch images and periodically clean them up. Then, categorize the historical touch images by gesture type, and finally classify and save the categorized historical touch images in a database. The steps are as follows:

[0059] The system continuously collects touch patterns generated by users during use, forming a historical touch pattern collection. The collected historical touch patterns are organized at fixed intervals, and invalid pattern data is removed to ensure data quality and reduce the burden on users' phone storage. Then, the cleaned historical touch patterns are filtered and classified according to gesture type to ensure that the pattern matches the corresponding gesture type. The filtered historical touch patterns are stored in the database according to gesture type for easy retrieval and analysis later.

[0060] S3. Segment the finger regions based on hand image data, and perform basic deviation threshold analysis on the segmented finger regions. At the same time, combine the standard touch pattern with historical touch patterns to perform maximum deviation threshold analysis; set the judgment criteria for gesture shape matching to balance security and ease of use.

[0061] The steps for S3 are as follows:

[0062] S3.1. Segment the finger regions based on the hand image data, obtain the finger region corresponding to each finger in the hand image data, then compare the deviations of the finger regions of each finger with each other, and select the maximum deviation value as the basic deviation threshold from the comparison results; the steps are as follows:

[0063] The collected hand image data is processed to segment the region corresponding to each finger in the image, clarify the position and range of each finger in the image, and then the segmented finger regions are compared with each other to analyze the deviations in position and shape. From the deviation results of all finger regions, the largest deviation value is selected and set as the basic deviation threshold. Generally, the deviation values ​​of the little finger and the thumb are used as the basic deviation threshold because the little finger and the thumb usually have the largest deviation.

[0064] S3.2. Compare the standard touch pattern with the historical touch pattern to determine the deviation (calculate the deviation between the two in terms of trajectory and shape), and select the maximum deviation value as the maximum deviation threshold from the comparison results.

[0065] S4. Extract the standard completion time of standard touch patterns, and analyze the average completion time of historical touch patterns corresponding to the gesture type. Then, set a time threshold based on the average completion time and the standard completion time; filter out fast or slow gestures that are obviously inconsistent with user habits to improve security.

[0066] The steps for S4 are as follows:

[0067] S4.1 Extract the completion time of standard touch patterns and historical touch patterns. Then, summarize the completion times of historical touch patterns corresponding to each gesture type and perform an average completion time analysis to obtain the average completion time of historical touch patterns corresponding to each gesture type. The steps are as follows:

[0068] Extract the time corresponding to the completion of each historical touch gesture from the historical touch graph data stored in the database. Categorize and summarize the extracted historical completion times according to gesture type, grouping all historical completion times under the same gesture type together. Then, calculate the average completion time corresponding to that gesture type, serving as a reference for users' habitual time spent performing that gesture. The formula is as follows:

[0069] ;

[0070] in, To represent the average completion time of historical touch patterns corresponding to a certain gesture type, n represents the number of valid historical touch patterns under that gesture type. This represents the completion time of the i-th historical touch graphic under this gesture type.

[0071] S4.2 Set the standard completion time as the initial time threshold, and then adjust the initial time threshold by using the average completion time;

[0072] When the average completion time is greater than the standard completion time, the time threshold is extended. This indicates that users generally take longer to perform the gesture than the standard time. Therefore, the time threshold is extended to adapt to users' operating habits.

[0073] When the average completion time is less than the standard completion time, the time threshold is shortened. This indicates that users generally take less time to perform the gesture than the standard time. Therefore, the time threshold is shortened to make the judgment criteria more closely reflect the actual operation of users. The specific steps are as follows:

[0074] when When, the formula is as follows: ;

[0075] when When, the formula is as follows: ;

[0076] in, To represent the adjusted time threshold, To represent the completion time of a standard touch graphic, k is an adjustment coefficient (with a value range of 0 < k < 1, used to control the adjustment range).

[0077] S5. Compare the real-time completion time with a time threshold, retain the real-time touch graphic based on the comparison result, analyze the deviation value of the real-time touch graphic with the standard touch graphic, and select the basic deviation threshold corresponding to the standard touch graphic with the smallest deviation value for adjustment based on the real-time completion time; to achieve a more intelligent judgment logic that takes into account the differences in the user's state during operation.

[0078] The steps for S5 are as follows:

[0079] S5.1. The real-time completion time is compared with the time threshold. If the real-time completion time exceeds the time threshold, it means that the gesture operation does not meet the preset time standard. The real-time touch graphic is then determined to be invalid and not retained. Conversely, if the real-time completion time does not exceed the time threshold, it means that the gesture operation meets the time requirements. The real-time touch graphic is then determined to be retained for subsequent deviation analysis.

[0080] S5.2. Combine the retained real-time touch graphics with all standard touch graphics to perform deviation value analysis. Compare the retained real-time touch graphics with all standard touch graphics in the system one by one, analyze their differences in trajectory, shape, etc., calculate and obtain the deviation value corresponding to each comparison result, and obtain the deviation value between the real-time touch graphics and all standard touch graphics.

[0081] S5.3. Select the standard touch pattern with the smallest deviation value from the list of deviation values ​​obtained in S5.2, and then adjust the basic threshold corresponding to the standard touch pattern in combination with the real-time completion time.

[0082] From all the obtained deviation values, find the deviation value with the smallest value, determine the standard touch pattern corresponding to the deviation value, and extract the basic deviation threshold corresponding to the determined standard touch pattern.

[0083] The shorter the real-time completion time, the faster the user operation. If the judgment criteria are relaxed, the basic deviation threshold will be adjusted to be larger.

[0084] The longer the real-time completion time, the slower the user operation, and the stricter the judgment criteria need to be. Therefore, the basic deviation threshold should be adjusted to be smaller. The specific steps are as follows:

[0085] when When, the formula is as follows: ;

[0086] when When, the formula is as follows: ;

[0087] in, To represent the adjusted baseline deviation threshold, To represent the original baseline deviation threshold corresponding to the standard touch pattern, To indicate the real-time completion time, Adjustment coefficient (range of values) (used to control the adjustment range).

[0088] S6. Compare the deviation value with the basic deviation threshold adjusted in S5 and the maximum deviation threshold. If the deviation value is greater than the basic deviation threshold but less than the maximum deviation threshold, other gesture types are required for verification. Multi-level judgment balances security and user experience, reducing misjudgments.

[0089] The steps for S6 are as follows:

[0090] S6.1 Compare the minimum deviation value with the basic deviation threshold and the maximum deviation threshold of the corresponding standard touch pattern;

[0091] S6.2 When the deviation value is less than the basic deviation threshold, it means that the gesture operation matches the standard pattern well, and the phone screen is determined to be unlocked successfully.

[0092] S6.3 When the deviation value is greater than the basic deviation threshold but less than the maximum deviation threshold, it indicates that the matching degree between this gesture operation and the standard graphic is in the middle range. Then, other gesture types are required to be drawn for verification. If the drawn other gesture types pass, the phone screen is determined to be unlocked successfully.

[0093] If a user draws another type of gesture and the gesture passes verification, the phone screen unlock is considered successful; otherwise, the unlock fails.

[0094] S6.4 If the deviation value is greater than the maximum deviation threshold, the phone screen unlocking is determined to have failed.

[0095] S6.2 When other gesture types are required for verification, if only one gesture type is available, the comparison is performed only based on the basic deviation threshold. The steps are as follows:

[0096] When the system requires other gesture types to be drawn for verification, it first checks the total number of preset gesture types in the system. If only one gesture type exists in the system and cannot be supplemented by other gestures for verification, the basic deviation threshold is used as the sole judgment standard. If the minimum deviation value is less than the basic deviation threshold, the unlock is determined to be successful; if the minimum deviation value is greater than or equal to the basic deviation threshold, the unlock is determined to be unsuccessful.

[0097] Compared to traditional fixed-mode gesture unlocking, it can better adapt to the operating habits of different users. At the same time, through dynamic adjustment and multi-level verification mechanisms, it can improve the user experience while maintaining a high level of security.

[0098] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for unlocking a mobile phone screen based on gesture recognition, characterized in that: Includes the following steps: S1. Perform touch detection on the mobile phone screen to generate real-time touch graphics and real-time completion time; S2. Collect standard touch graphics and hand image data saved for the set gesture type, and collect historical touch graphics, and classify the historical touch graphics in the database according to the gesture type. S3. Segment the finger regions based on the hand image data, and perform basic deviation threshold analysis on the segmented finger regions. At the same time, perform maximum deviation threshold analysis by combining the standard touch pattern with the historical touch pattern. S4. Extract the standard completion time of the standard touch pattern, and at the same time analyze the average completion time of the historical touch patterns corresponding to the gesture type. Then, set the time threshold based on the average completion time and the standard completion time. S5. Compare the real-time completion time with a time threshold, retain the real-time touch graphic based on the comparison results, analyze the deviation value of the real-time touch graphic with the standard touch graphic, and select the basic deviation threshold corresponding to the standard touch graphic with the smallest deviation value for adjustment in conjunction with the real-time completion time. S6. Compare the deviation value with the basic deviation threshold adjusted in S5 and the maximum deviation threshold. If the deviation value is greater than the basic deviation threshold but less than the maximum deviation threshold, other gesture types are required for verification.

2. The mobile phone screen unlocking method based on gesture recognition according to claim 1, characterized in that: In S1, the mobile phone screen is a touch screen, and touch is recorded through capacitive sensing. At the same time, for the historical touch patterns collected in S2, only the historical touch patterns that have been verified are retained.

3. The mobile phone screen unlocking method based on gesture recognition according to claim 1, characterized in that: S1 includes performing touch detection on the mobile phone screen, recording touch data when the mobile phone screen is detected to be touched, then capturing the recorded touch data in real time as a touch graphic, and obtaining the real time of completion of the touch data.

4. The mobile phone screen unlocking method based on gesture recognition according to claim 1, characterized in that: The steps in S2 are as follows: S2.1 Collect the standard touch graphics saved by the user when setting the gesture type, and collect the user's hand image data after setting the gesture type; S2.2 Collect historical touch images and clean them up regularly. Then, filter the historical touch images by gesture type and classify and save the filtered historical touch images in the database.

5. The mobile phone screen unlocking method based on gesture recognition according to claim 1, characterized in that: The steps in S3 are as follows: S3.

1. Perform finger region segmentation based on hand image data, obtain the finger region corresponding to each finger in the hand image data, then compare the deviations of the finger regions of each finger with each other, and select the maximum deviation value as the basic deviation threshold from the comparison results. S3.

2. Compare the standard touch pattern with the historical touch pattern to determine the deviation, and select the maximum deviation value as the maximum deviation threshold from the comparison results.

6. The mobile phone screen unlocking method based on gesture recognition according to claim 1, characterized in that: The steps in S4 are as follows: S4.1 Extract the completion time of standard touch graphics and extract the completion time of historical touch graphics. Then, summarize the completion times of historical touch graphics corresponding to the gesture type and perform average completion time analysis to obtain the average completion time of historical touch graphics corresponding to the gesture type. S4.2 Set the standard completion time as the initial time threshold, and then adjust the initial time threshold by using the average completion time; If the average completion time is greater than the standard completion time, the time threshold will be adjusted by extending the time. If the average completion time is less than the standard completion time, the time threshold will be adjusted by shortening the time.

7. The mobile phone screen unlocking method based on gesture recognition according to claim 1, characterized in that: The steps in S5 are as follows: S5.

1. The real-time completion time is compared with a time threshold. If the real-time completion time exceeds the time threshold, the real-time touch graphic is deemed invalid and not retained. Conversely, if the real-time completion time does not exceed the time threshold, the real-time touch graphic is deemed retained. S5.

2. Combine the retained real-time touch graphics with all standard touch graphics to perform deviation value analysis and obtain the deviation value between the real-time touch graphics and all standard touch graphics. S5.

3. Select the standard touch pattern with the smallest deviation value from the list of deviation values ​​obtained in S5.2, and then adjust the basic threshold corresponding to the standard touch pattern in combination with the real-time completion time. The shorter the real-time completion time, the larger the basic deviation threshold will be. The longer the real-time completion time, the smaller the base deviation threshold will be.

8. The mobile phone screen unlocking method based on gesture recognition according to claim 1, characterized in that: The steps in S6 are as follows: S6.1 Compare the minimum deviation value with the basic deviation threshold and the maximum deviation threshold of the corresponding standard touch pattern; S6.

2. If the deviation value is less than the basic deviation threshold, the phone screen is determined to be unlocked successfully. S6.3 When the deviation value is greater than the basic deviation threshold but less than the maximum deviation threshold, other gesture types are required for verification. If the other gesture types are drawn successfully, the phone screen is determined to be unlocked successfully. S6.4 If the deviation value is greater than the maximum deviation threshold, the phone screen unlocking is determined to have failed.

9. A mobile phone screen unlocking method based on gesture recognition according to claim 8, characterized in that: S6.2 requires other gesture types to be drawn for verification. If there is only one gesture type, the comparison is based solely on the basic deviation threshold.

Images