WO2016201731A1 - Appareil et procédé de reconnaissance d'empreinte digitale et dispositif électronique - Google Patents

Appareil et procédé de reconnaissance d'empreinte digitale et dispositif électronique Download PDF

Info

Publication number
WO2016201731A1
WO2016201731A1 PCT/CN2015/082900 CN2015082900W WO2016201731A1 WO 2016201731 A1 WO2016201731 A1 WO 2016201731A1 CN 2015082900 W CN2015082900 W CN 2015082900W WO 2016201731 A1 WO2016201731 A1 WO 2016201731A1
Authority
WO
WIPO (PCT)
Prior art keywords
fingerprint
feature
user
matching degree
matching
Prior art date
Application number
PCT/CN2015/082900
Other languages
English (en)
Chinese (zh)
Inventor
钟焰涛
傅文治
Original Assignee
宇龙计算机通信科技(深圳)有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 宇龙计算机通信科技(深圳)有限公司 filed Critical 宇龙计算机通信科技(深圳)有限公司
Publication of WO2016201731A1 publication Critical patent/WO2016201731A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1365—Matching; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1347—Preprocessing; Feature extraction

Definitions

  • the invention belongs to the field of intelligent identification technology of biometrics, and in particular relates to a fingerprint identification method, device and electronic device.
  • Fingerprint recognition technology is the most widely used biometric recognition technology.
  • the technology realizes fingerprint recognition based on various feature points such as endpoints, bifurcation points, bifurcation points, isolated points, ring points and short lines contained in the fingerprint. Fingerprint recognition has been integrated into many high-end mobile phones.
  • Fingerprint recognition technology includes fingerprint feature extraction and fingerprint recognition and recognition.
  • the conventional fingerprint identification scheme stores, in the fingerprint feature database, a fingerprint feature used as a fingerprint template, which is pre-acquired (for example, can be collected when the user registers the fingerprint recognition function). Therefore, after extracting the fingerprint feature of the fingerprint input by the user, the extracted user fingerprint feature may be compared with each fingerprint template included in the fingerprint feature database, if the fingerprint feature of the user matches the feature points of a certain fingerprint template. When the set threshold is reached, the user is identified.
  • the traditional fingerprint identification scheme cannot effectively identify different fingerprints of the user, for example, when the fingerprint of the user is dry or peeled, some feature points are difficult to extract, and the extracted fingerprint features temporarily occur.
  • the traditional fingerprint identification scheme is easy to generate problems that cannot be effectively identified.
  • the object of the present invention is to provide a fingerprint identification method, device and electronic device, which aims to solve the problem that the traditional fingerprint identification scheme cannot effectively identify different states of the user's fingerprint, and enhance the fingerprint recognition. Accuracy.
  • a fingerprint identification method comprising:
  • the fingerprint feature database includes at least one fingerprint template, each of the fingerprints
  • the template includes a second fingerprint feature and a P group feature point weight, each set of feature point weights corresponding to a user fingerprint state, and each set of feature point weights is used to calculate a matching degree value of the user fingerprint in the corresponding user fingerprint state, P is a natural number greater than one;
  • the fingerprint recognition is successful.
  • the first fingerprint feature and the second fingerprint feature respectively comprise a corresponding number of feature points
  • the feature points include an endpoint, a bifurcation point, a bifurcation point, an isolated point, a ring point, and a short Pattern.
  • the first fingerprint feature is matched and identified based on the preset fingerprint feature database, and the optimal matching degree of the first fingerprint feature is obtained, including:
  • the matching degree with the largest value is selected as the optimal matching degree of the first fingerprint feature.
  • the matching result includes an n-dimensional 0-1 vector (a 1 , a 2 , . . . , a n ), where n is a natural number greater than 1, wherein
  • a i 1 indicates that the i-th feature point in the first fingerprint feature matches the i-th feature point in the second fingerprint feature;
  • the above method preferably, further includes:
  • the set of feature point weights used in calculating the optimal matching degree is adjusted according to the matching result used when calculating the optimal matching degree.
  • the above method preferably, further includes:
  • the feature point weight training is performed on the fingerprint template in a preset P user fingerprint state, and the P group feature point weights corresponding to the P user fingerprint states are obtained.
  • the above method preferably, further includes:
  • fingerprint recognition fails.
  • a fingerprint identification device comprising:
  • a fingerprint acquiring module configured to acquire a fingerprint input by a user
  • a feature extraction module configured to perform feature point extraction on the fingerprint to obtain a first fingerprint feature to be identified
  • a matching identification module configured to perform matching and identifying the first fingerprint feature based on a preset fingerprint feature database, and obtain an optimal matching degree of the first fingerprint feature; wherein the fingerprint feature database includes at least one fingerprint template
  • Each of the fingerprint templates includes a second fingerprint feature and a P group feature point weight, each set of feature point weights corresponds to a user fingerprint state, and each set of feature point weights is used to calculate a user of the corresponding user fingerprint state.
  • the matching value of the fingerprint, P is a natural number greater than 1;
  • the first result determining module is configured to determine that the fingerprint recognition is successful when the optimal matching degree reaches a set threshold.
  • the matching identification module includes:
  • a matching unit configured to match the first fingerprint feature with a second fingerprint feature included in each fingerprint template in the fingerprint feature database to obtain a matching result
  • a calculating unit configured to perform weighting calculation on the matching result by using each set of feature point weights of the fingerprint template, to obtain a matching degree of the first fingerprint feature in various user fingerprint states
  • a selecting unit configured to select a matching degree with the largest value from the series of matching degrees of the first fingerprint feature corresponding to each fingerprint template and each user fingerprint state, and use the same as the optimal matching of the first fingerprint feature degree.
  • the above device preferably, further comprises:
  • a weight adjustment module configured to adjust a set of feature point weights used when calculating the optimal matching degree according to a matching result used when calculating the optimal matching degree when fingerprint identification is successful .
  • the above device preferably, further comprises:
  • a pre-processing module configured to advance the fingerprint template in a preset P user fingerprint state
  • the row feature point weight training is performed, and the P group feature point weights corresponding to the P user fingerprint states are obtained one by one.
  • the above device preferably, further comprises:
  • the second result determining module is configured to determine that the fingerprint recognition fails when the optimal matching degree does not reach the set threshold.
  • An electronic device includes a communication bus 1002, a transceiver, a memory, and a processor, wherein:
  • the communication bus 1002 is configured to implement connection communication between the transceiver device, the memory, and the processor;
  • the memory stores a set of program codes, and the processor calls the program code stored in the memory to perform the following operations:
  • the transceiver device is configured to acquire a fingerprint input by a user
  • the processor is configured to perform feature point extraction on the fingerprint to obtain a first fingerprint feature to be identified
  • the processor is further configured to perform matching and identifying the first fingerprint feature based on a preset fingerprint feature database, and acquire an optimal matching degree of the first fingerprint feature; wherein the fingerprint feature database includes at least one a fingerprint template, each of the fingerprint templates includes a second fingerprint feature and a P group feature point weight, each set of feature point weights corresponding to a user fingerprint state, and each set of feature point weights is used to calculate a corresponding user fingerprint state
  • the matching value of the user fingerprint, P is a natural number greater than 1;
  • the processor is further configured to determine that the fingerprint recognition succeeds when the optimal matching degree reaches a set threshold.
  • the processor performs matching and identifying the first fingerprint feature based on a preset fingerprint feature database, and acquiring an optimal matching degree of the first fingerprint feature specifically includes:
  • the processor is further configured to match the first fingerprint feature with a second fingerprint feature included in each fingerprint template in the fingerprint feature database to obtain a matching result;
  • the processor is further configured to perform weighting calculation on the matching result by using each set of feature point weights of the fingerprint template, to obtain a matching degree of the first fingerprint feature in various user fingerprint states;
  • the processor is further configured to select a matching degree with the largest value from the series of matching degrees of the first fingerprint feature corresponding to each fingerprint template and each user fingerprint state, and use the first fingerprint feature as the first fingerprint feature. Optimal matching.
  • the processor is further configured to perform the following steps:
  • the set of feature point weights used when calculating the optimal matching degree is adjusted.
  • the processor is further configured to perform the following steps:
  • the feature point weight training is performed on the fingerprint template in a preset P user fingerprint state, and the P group feature point weights corresponding to the P user fingerprint states are obtained.
  • the processor is further configured to perform the following steps:
  • the judgment recognition result is that the fingerprint recognition fails.
  • the present invention performs matching matching on the fingerprint features of the user to be identified based on the preset fingerprint feature database, and obtains the optimal matching degree of the fingerprint feature of the user, and then reaches the set threshold in the optimal matching degree.
  • the fingerprint feature database includes at least one fingerprint template, where the fingerprint template includes a second fingerprint feature and a P group feature point weight, each set of feature point weights corresponds to a user fingerprint state, and each set of feature point weights is used. Calculate the matching value of the user's fingerprint in the corresponding state.
  • the present invention matches multiple sets of different feature point weights for each fingerprint template in the database, so that when the fingerprint of the user changes due to factors such as drying or peeling, the extracted fingerprint features change.
  • the feature point weight group of the appropriate state By using the feature point weight group of the appropriate state, a more accurate matching degree is calculated, the problem of the traditional scheme is solved, and the accuracy of fingerprint recognition is improved.
  • Embodiment 1 is a flowchart of Embodiment 1 of a fingerprint identification method provided by the present application;
  • Embodiment 2 is a flowchart of Embodiment 2 of a fingerprint identification method provided by the present application;
  • Embodiment 3 is a schematic diagram of a weight group training process provided by Embodiment 2 of the present application.
  • Embodiment 4 is a flowchart of Embodiment 3 of a fingerprint identification method provided by the present application.
  • FIG. 5 is a flowchart of Embodiment 4 of a fingerprint identification method provided by the present application.
  • FIG. 6 is a schematic structural diagram of a fingerprint identification apparatus according to Embodiment 5 of the present application.
  • FIG. 10 is a schematic structural diagram of an electronic device according to Embodiment 6 of the present application.
  • a first embodiment of the present invention discloses a fingerprint identification method, which can be applied to an electronic device such as a smart phone or a tablet computer, for example, specifically applicable to user identity authentication of a corresponding service of an electronic device.
  • the method can include the following steps:
  • S101 Acquire a fingerprint input by a user.
  • the fingerprint input by the user through the fingerprint sensor can be obtained.
  • S102 Perform feature point extraction on the fingerprint to obtain a first fingerprint feature to be identified.
  • the fingerprint texture is not continuous, smooth and straight, but features such as interruption, bifurcation or turning.
  • the feature points generated by these features provide confirmation information about the uniqueness of the fingerprint.
  • the most typical feature points are the endpoint and Bifurcation points, other feature points also include divergence points, isolated points, ring points, short lines, etc.
  • the parameters of the feature points include direction, curvature, and position.
  • user fingerprint features including various feature points such as an endpoint, a bifurcation point, a bifurcation point, an isolated point, a ring point, and a short pattern can be obtained.
  • S103 Matching and identifying the first fingerprint feature based on a preset fingerprint feature database, and acquiring an optimal matching degree of the first fingerprint feature; wherein the fingerprint feature database includes at least one fingerprint template, where each The fingerprint template includes a second fingerprint feature and a P group feature point weight.
  • Each set of feature point weights corresponds to a user fingerprint state, and each set of feature point weights is used to calculate the matching degree of the user fingerprints in the corresponding user fingerprint state. For the value, P is a natural number greater than one.
  • the user's fingerprints tend to be too dry, too moist or peeling, which makes it difficult to extract certain fingerprint features, which affects the accuracy of fingerprint recognition. Based on this, the user first divides the user's fingerprint status into normal. Dry, moist and peeling four states.
  • each fingerprint template in the fingerprint feature database by learning the extraction and recognition of fingerprint features in various states, four sets of feature point weights are assigned to each fingerprint template in the fingerprint feature database, and each set of feature point weights corresponds to a corresponding user fingerprint state.
  • the fingerprint feature contained in the fingerprint template can be represented as an n-dimensional vector containing n eigenvalues: (c 1 , c 2 , . . . , c n ),
  • the feature value c i is a specific representation of the i-th feature point in the fingerprint template; and corresponding to the normal, dry, wet and peeled state of the user fingerprint, the fingerprint template includes four sets of feature point weights: (w 11 ; w 12 , ..., w 1n ), (w 21 , w 22 , ..., w 2n ), (w 31 , w 32 , ..., w 3n ) and (w 41 , w 42 , ..., w 4n ).
  • the invention increases the weight of the feature points that are easy to be extracted and recognized in the corresponding fingerprint state, and reduces the weight of the feature points that are difficult to be extracted and recognized, thereby realizing the matching of the fingerprint features in the state.
  • the user fingerprint feature is sequentially matched with each fingerprint template included in the fingerprint feature database. Specifically, in the process of matching with each fingerprint template, first, matching each feature point included in the user fingerprint feature with each feature point included in the fingerprint template, an n-dimensional 0-1 can be obtained.
  • the matching result represented by the vector (a 1 , a 2 , ..., a n ), wherein
  • the matching degree values in the four fingerprint states of normal, dry, wet, and peeling can be obtained separately: p 1 , p 2 , p 3 , and p 4 .
  • the matching degree calculated for each fingerprint state under each fingerprint template is integrated, and the matching degree with the largest value is selected as the user fingerprint feature.
  • the optimal matching degree wherein the fingerprint state corresponding to the optimal matching degree is the most suitable state corresponding to the user fingerprint, that is, the fingerprint corresponding to the optimal matching degree
  • the status reflects the actual status of the user's fingerprint.
  • the step specifically compares the optimal matching degree of the user fingerprint feature with a preset matching threshold. If the optimal matching degree reaches the set threshold, the user fingerprint matching is successfully performed. Thus, the user can be identified.
  • the threshold t can be set to a real number satisfying 0 ⁇ t ⁇ 1. In the actual recognition scenario, the t value is too high to cause unrecognized, so the threshold t is generally set to a value less than 0.2.
  • the division of the fingerprint state of the user, and the feature point weight of the corresponding group number given to the fingerprint template on the basis of the present application are only exemplary descriptions of the solution of the present application. Personnel can divide or set the user fingerprint status and the number of feature point weights according to the various states in which the user's fingerprints appear in real life.
  • the present invention performs matching matching on the fingerprint features of the user to be identified based on the preset fingerprint feature database, and obtains the optimal matching degree of the fingerprint feature of the user, and then reaches the set threshold in the optimal matching degree.
  • the fingerprint feature database includes at least one fingerprint template, where the fingerprint template includes a second fingerprint feature and a P group feature point weight, each set of feature point weights corresponds to a user fingerprint state, and each set of feature point weights is used. Calculate the matching value of the user's fingerprint in the corresponding state.
  • the present invention matches multiple sets of different feature point weights for each fingerprint template in the database, so that when the fingerprint of the user changes due to factors such as drying or peeling, the extracted fingerprint features change.
  • the feature point weight group of the appropriate state By using the feature point weight group of the appropriate state, a more accurate matching degree is calculated, the problem of the traditional scheme is solved, and the accuracy of fingerprint recognition is improved.
  • the fingerprint identification method may include the following preprocessing process:
  • S101' Perform feature point weight training on the fingerprint template in a preset P user fingerprint state, and obtain P group feature point weights corresponding to the P user fingerprint states one by one.
  • the feature point weight of the corresponding group number is given in advance according to the number of user fingerprint states.
  • the feature point weight group corresponding to the state is trained by learning the fingerprint feature extraction and recognition process in each fingerprint state, and each group weight The acquisition of values requires separate training procedures.
  • the training process of the weight group may include the following steps:
  • the fingerprint template contains a total of n feature points, denoted as (c 1 , c 2 , . . . , c n ).
  • Group (1/n, 1/n, ..., 1/n) and set the matching threshold t to a real number satisfying 0 ⁇ t ⁇ 1, because the value of t is too high, it is easy to cause no recognition, so
  • This embodiment specifically sets t to a value less than 0.2.
  • S302 Acquire a user fingerprint and extract a user fingerprint feature.
  • the fingerprint input by the user through the fingerprint sensor in the dry state of the fingerprint is obtained, and the fingerprint feature is extracted.
  • step S303 Identify the fingerprint feature of the user, and determine whether the recognition is successful. If the recognition is successful, step S304 is performed, otherwise, the process proceeds to step S305.
  • the fingerprint template, the weight group and the set threshold are used to identify the fingerprint feature of the user.
  • the specific identification process refer to the description of the first embodiment, which is not described in detail herein.
  • the weights of the corresponding feature points in the weight group are adjusted, and the calculation formula used for the adjustment is as follows:
  • w i ' is the modified and adjusted feature point weight
  • w i is the feature point weight used for matching and identifying the user fingerprint
  • v i is an intermediate value in the process of calculating w i '
  • the effect is to maintain the adjusted sum of the weights as 1, ie
  • the invention increases the weight of the feature points that are easy to be extracted and recognized in the corresponding fingerprint state, reduces the weight of the feature points that are difficult to be extracted and recognized, and realizes the adjustment of the matching degree of the fingerprint features in the state, and ensures the state. A more accurate match value can be obtained.
  • SS305 Determine whether the training process is sufficient, if it is enough, end the training process; otherwise, go to step S302 to enter the next round of training.
  • the invention continuously performs the iterative modification process of the feature point weights, and after sufficient training, finally obtains the feature point weight group which is required to be corresponding to the state and has a reasonable weight distribution.
  • the present invention continues to perform the multiple fingerprint training in different states to adjust the weight of the fingerprint template, and can form a precise identification of various state fingerprints after convergence. Multiple sets of weights.
  • the fingerprint identification method may further include the following steps:
  • S105 If the fingerprint identification is successful, adjusting a set of feature point weights used when calculating the optimal matching degree according to the matching result used when calculating the optimal matching degree.
  • the feature matching situation according to the successful recognition may also be used according to the feature matching in the successful recognition.
  • the weight of the fingerprint template is adjusted to adapt to the small change of the fingerprint.
  • the fingerprint identification method may further include the following steps:
  • the optimal matching degree corresponding to the user fingerprint feature is small, and the threshold value is not reached, the matching between the fingerprint of the user and the fingerprint template in the database is poor, so that the fingerprint identification fails and the user is not recognized.
  • the fifth embodiment discloses a fingerprint identification device, which is disclosed in the first embodiment to the fourth embodiment.
  • the fingerprint identification method corresponds.
  • the device includes a fingerprint acquiring module 100, a feature extraction module 200, a matching identification module 300, and a first result decision module 400.
  • the fingerprint obtaining module 100 is configured to acquire a fingerprint input by a user.
  • the feature extraction module 200 is configured to perform feature point extraction on the fingerprint to obtain a first fingerprint feature to be identified.
  • the matching identification module 300 is configured to perform matching and identifying the first fingerprint feature based on a preset fingerprint feature database, and acquire an optimal matching degree of the first fingerprint feature; wherein the fingerprint feature database includes at least one fingerprint a template, each of the fingerprint templates includes a second fingerprint feature and a P group feature point weight, each set of feature point weights corresponding to a user fingerprint state, and each set of feature point weights is used to calculate a corresponding user fingerprint state
  • the matching value of the user's fingerprint, P is a natural number greater than 1.
  • the matching identification module 300 includes a matching unit, a computing unit, and
  • a matching unit configured to match the first fingerprint feature with a second fingerprint feature included in each fingerprint template in the fingerprint feature database to obtain a matching result and a selection unit.
  • a calculating unit configured to perform weighting calculation on the matching result by using each set of feature point weights of the fingerprint template, to obtain a matching degree of the first fingerprint feature in various user fingerprint states
  • a selecting unit configured to select a matching degree with the largest value from the series of matching degrees of the first fingerprint feature corresponding to each fingerprint template and each user fingerprint state, and use the same as the optimal matching of the first fingerprint feature degree.
  • the first result decision module 400 is configured to determine that the fingerprint recognition is successful when the optimal matching degree reaches a set threshold.
  • the device further includes a pre-processing module 500, configured to perform feature point weight training on the fingerprint template in advance in a preset P user fingerprint state, and obtain the P group feature point weights corresponding to P user fingerprint states.
  • a pre-processing module 500 configured to perform feature point weight training on the fingerprint template in advance in a preset P user fingerprint state, and obtain the P group feature point weights corresponding to P user fingerprint states.
  • the device further includes a weight adjustment module 600, configured to calculate the matching result when the fingerprint is successfully determined according to the matching result used when calculating the optimal matching degree.
  • the set of feature point weights used in the optimal matching degree are adjusted.
  • the apparatus further includes a second result decision module 700, configured to determine that the recognition result is a fingerprint recognition failure when the optimal matching degree does not reach the set threshold.
  • the embodiment discloses an electronic device, the electronic device at least one transceiver device 1003, at least one processor 1001, such as a CPU, a memory 1004 and at least one communication bus 1002.
  • the communication bus 1002 is used to connect the transceiver 1003, the processor 1001, and the memory 1004.
  • the above memory 1004 may be a high speed RAM memory or a non-volatile memory such as a disk memory.
  • the above-mentioned memory 1004 is further configured to store a set of program codes, and the transceiver device 1003 and the processor 1001 are configured to call the program code stored in the memory 1004, and perform the following operations:
  • the transceiver device 1003 is configured to acquire a fingerprint input by a user
  • the processor 1001 is configured to perform feature point extraction on the fingerprint to obtain a first fingerprint feature to be identified;
  • the processor 1001 is further configured to perform matching and identifying the first fingerprint feature based on a preset fingerprint feature database, and acquire an optimal matching degree of the first fingerprint feature; wherein the fingerprint feature database includes at least a fingerprint template, each of the fingerprint templates includes a second fingerprint feature and a P group feature point weight, each set of feature point weights corresponding to a user fingerprint state, and each set of feature point weights is used to calculate a corresponding user fingerprint
  • the matching value of the user's fingerprint in the state, P is a natural number greater than 1;
  • the processor 1001 is further configured to: when the optimal matching degree reaches a set threshold, determine that the recognition result is successful fingerprint identification.
  • the processor 1001 performs the matching and identifying the first fingerprint feature based on the preset fingerprint feature database, and acquiring the optimal matching degree of the first fingerprint feature specifically includes:
  • the processor 1001 is further configured to match the first fingerprint feature with a second fingerprint feature included in each fingerprint template in the fingerprint feature database to obtain a matching result;
  • the processor 1001 is further configured to perform weighting calculation on the matching result by using each set of feature point weights of the fingerprint template, to obtain a matching degree of the first fingerprint feature in various user fingerprint states;
  • the processor 1001 is further configured to correspond to each fingerprint template and each user from the first fingerprint feature. Among the series of matching degrees of the fingerprint state, the matching degree with the largest value is selected as the optimal matching degree of the first fingerprint feature.
  • the processor 1001 is further configured to perform the following steps:
  • the set of feature point weights used when calculating the optimal matching degree is adjusted.
  • the processor 1001 is further configured to perform the following steps:
  • the feature point weight training is performed on the fingerprint template in a preset P user fingerprint state, and the P group feature point weights corresponding to the P user fingerprint states are obtained.
  • the processor 1001 is further configured to perform the following steps:
  • the judgment recognition result is that the fingerprint recognition fails.
  • the electronic device of the embodiment can accurately identify the user's fingerprint under various states (such as normal, too dry, too humid, peeling, etc.), and does not change due to the state of the user's fingerprint.
  • states such as normal, too dry, too humid, peeling, etc.
  • the problem of difficult identification is high, and the recognition accuracy is high, which improves the user experience.
  • the present application can be implemented by means of software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product in essence or in the form of a software product, which may be stored in a storage medium such as a ROM/RAM or a disk. , an optical disk, etc., includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to perform the methods described in various embodiments of the present application or portions of the embodiments.
  • a computer device which may be a personal computer, server, or network device, etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Security & Cryptography (AREA)
  • Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Collating Specific Patterns (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un appareil et un procédé de reconnaissance d'empreinte digitale, ainsi qu'un dispositif électronique. Une caractéristique d'empreinte digitale d'utilisateur devant être reconnue est amenée à correspondre et reconnue sur la base d'une base de données de caractéristiques d'empreinte digitale prédéfinie, un degré de correspondance optimale de la caractéristique d'empreinte digitale d'utilisateur est acquis, puis l'utilisateur est reconnu une fois que le degré de correspondance optimale atteint une valeur seuil définie. La base de données de caractéristiques d'empreinte digitale contient au moins un modèle d'empreinte digitale, le modèle d'empreinte digitale contient P groupes de pondérations de points caractéristiques, chaque groupe de pondérations correspond à un état d'empreinte digitale d'utilisateur et chaque groupe de pondérations de points caractéristiques est utilisé pour calculer une valeur de degré de correspondance d'une empreinte digitale d'utilisateur dans son état correspondant. Par rapport à différents états d'empreintes digitales d'utilisateur, une pluralité de groupes de pondérations de points caractéristiques sont amenés à correspondre au préalable à chaque modèle d'empreinte digitale dans la base de données, de sorte que, lorsqu'une caractéristique d'empreinte digitale extraite d'une empreinte digitale d'utilisateur change en raison de facteurs tels que la sécheresse ou la desquamation, un degré de correspondance relativement précis puisse être calculé à l'aide d'un groupe de pondérations de points caractéristiques dans un état approprié, améliorant ainsi le degré de précision de la reconnaissance d'empreinte digitale.
PCT/CN2015/082900 2015-06-17 2015-06-30 Appareil et procédé de reconnaissance d'empreinte digitale et dispositif électronique WO2016201731A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201510337023.6A CN105654025B (zh) 2015-06-17 2015-06-17 一种指纹识别方法、装置及电子设备
CN201510337023.6 2015-06-17

Publications (1)

Publication Number Publication Date
WO2016201731A1 true WO2016201731A1 (fr) 2016-12-22

Family

ID=56481588

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2015/082900 WO2016201731A1 (fr) 2015-06-17 2015-06-30 Appareil et procédé de reconnaissance d'empreinte digitale et dispositif électronique

Country Status (2)

Country Link
CN (1) CN105654025B (fr)
WO (1) WO2016201731A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106843987A (zh) * 2017-02-18 2017-06-13 珠海格力电器股份有限公司 基于指纹识别启动关联应用的方法及装置
CN114237383A (zh) * 2021-11-09 2022-03-25 浙江迈联医疗科技有限公司 基于前额单导联脑电信号的多状态识别方法
CN114915566A (zh) * 2021-01-28 2022-08-16 腾讯科技（深圳）有限公司 应用识别方法、装置、设备及计算机可读存储介质

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106682618A (zh) * 2016-12-27 2017-05-17 努比亚技术有限公司 指纹识别方法及移动终端
CN106909900A (zh) * 2017-02-27 2017-06-30 努比亚技术有限公司 指纹识别方法及装置
WO2018213945A1 (fr) * 2017-05-20 2018-11-29 深圳信炜科技有限公司 Capteur d'image et dispositif électronique
CN107330388B (zh) * 2017-06-21 2020-12-22 海信视像科技股份有限公司 指纹识别处理方法及终端
CN107480641B (zh) * 2017-08-16 2020-08-25 联想(北京)有限公司 一种指纹识别方法以及电子设备
CN109344594A (zh) * 2018-11-15 2019-02-15 Oppo（重庆）智能科技有限公司 一种基于指纹控制设备的方法及相关设备
KR20210157951A (ko) 2020-06-22 2021-12-30 삼성디스플레이 주식회사 지문 인증 장치, 이를 포함하는 표시 장치, 및 지문 인증 방법

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101918981A (zh) * 2008-01-21 2010-12-15 日本电气株式会社 图案匹配系统、图案匹配方法以及图案匹配程序
CN104050406A (zh) * 2014-07-03 2014-09-17 南昌欧菲生物识别技术有限公司 利用指纹组合进行鉴权的方法及终端设备
CN104376245A (zh) * 2014-10-16 2015-02-25 上海嘉炽自动化系统工程有限公司 工业人机界面的操作权限确定方法及系统
CN104392162A (zh) * 2014-12-08 2015-03-04 北京大唐智能卡技术有限公司 一种身份验证方法及智能卡

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101499130B (zh) * 2008-01-30 2012-06-27 深圳市普罗巴克科技股份有限公司 一种指纹识别方法和指纹识别系统
CN101604384B (zh) * 2009-07-17 2012-06-27 周易 个性化指纹识别方法
CN103065134B (zh) * 2013-01-22 2016-01-13 江苏超创信息软件发展股份有限公司 一种具有提示信息的指纹识别装置和方法
CN103136517B (zh) * 2013-03-04 2015-11-11 杭州景联文科技有限公司 一种基于关键列选择的滚动指纹图像序列实时拼接方法
CN103324944B (zh) * 2013-06-26 2016-11-16 电子科技大学 一种基于svm和稀疏表示的假指纹检测方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101918981A (zh) * 2008-01-21 2010-12-15 日本电气株式会社 图案匹配系统、图案匹配方法以及图案匹配程序
CN104050406A (zh) * 2014-07-03 2014-09-17 南昌欧菲生物识别技术有限公司 利用指纹组合进行鉴权的方法及终端设备
CN104376245A (zh) * 2014-10-16 2015-02-25 上海嘉炽自动化系统工程有限公司 工业人机界面的操作权限确定方法及系统
CN104392162A (zh) * 2014-12-08 2015-03-04 北京大唐智能卡技术有限公司 一种身份验证方法及智能卡

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106843987A (zh) * 2017-02-18 2017-06-13 珠海格力电器股份有限公司 基于指纹识别启动关联应用的方法及装置
CN114915566A (zh) * 2021-01-28 2022-08-16 腾讯科技（深圳）有限公司 应用识别方法、装置、设备及计算机可读存储介质
CN114915566B (zh) * 2021-01-28 2024-05-17 腾讯科技（深圳）有限公司 应用识别方法、装置、设备及计算机可读存储介质
CN114237383A (zh) * 2021-11-09 2022-03-25 浙江迈联医疗科技有限公司 基于前额单导联脑电信号的多状态识别方法
CN114237383B (zh) * 2021-11-09 2024-03-12 浙江迈联医疗科技有限公司 基于前额单导联脑电信号的多状态识别方法

Also Published As

Publication number Publication date
CN105654025B (zh) 2019-01-25
CN105654025A (zh) 2016-06-08

Similar Documents

Publication Publication Date Title
WO2016201731A1 (fr) Appareil et procédé de reconnaissance d'empreinte digitale et dispositif électronique
KR102439938B1 (ko) 사용자 인증을 위한 멀티-모달 퓨전 방법 및 사용자 인증 방법
US10198616B2 (en) Method for fingerprint unlocking and terminal
WO2017162076A1 (fr) Procédé et système d'identification de visage
KR102501838B1 (ko) 심전도 신호를 이용한 인증 장치 및 방법
WO2019179036A1 (fr) Modèle de réseau neuronal profond, dispositif électronique, procédé d'authentification d'identité et support de stockage
US8625861B2 (en) Fingerprint representation using gradient histograms
WO2016023264A1 (fr) Procédé d'identification d'empreintes digitales et dispositif d'identification d'empreintes digitales
US11893831B2 (en) Identity information processing method and device based on fundus image
US8995729B2 (en) Accelerated comparison using scores from coarse and fine matching processes
WO2019179029A1 (fr) Dispositif électronique, procédé de vérification d'identité et support d'informations lisible par ordinateur
CN109508658B (zh) 一种多生物特征联合识别方法及装置
CN105303173A (zh) 一种降低误识别率的方法和装置
US20200257885A1 (en) High speed reference point independent database filtering for fingerprint identification
US20100033304A1 (en) Identification Method Control Device and Identification Method Control Method
TWI437501B (zh) 基於生物特徵之身分驗證裝置及其方法
US9251398B2 (en) Method of matching, biometric matching apparatus, and computer program
KR102427853B1 (ko) 사용자 인증을 위한 등록 데이터베이스의 적응적 갱신 방법 및 장치
CN109150538B (zh) 一种指纹与声纹融合身份认证方法
US20190244006A1 (en) Skin information processing method, skin information processing device, and non-transitory computer-readable medium
CN106650554A (zh) 静态手势识别方法
CN114519898A (zh) 生物特征多模态融合识别方法、装置、存储介质及设备
KR101037759B1 (ko) 활성형상모델과 가버 특징 벡터를 이용한 얼굴 인식 방법
US20200387691A1 (en) A quick match algorithm for biometric data
Khan et al. Efficient and rotation invariant fingerprint matching algorithm using adjustment factor

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15895304

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15895304

Country of ref document: EP

Kind code of ref document: A1