WO2020168759A1 - 掌纹识别方法、装置、计算机设备和存储介质 - Google Patents

掌纹识别方法、装置、计算机设备和存储介质 Download PDF

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Publication number
WO2020168759A1
WO2020168759A1 PCT/CN2019/118424 CN2019118424W WO2020168759A1 WO 2020168759 A1 WO2020168759 A1 WO 2020168759A1 CN 2019118424 W CN2019118424 W CN 2019118424W WO 2020168759 A1 WO2020168759 A1 WO 2020168759A1
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area
palm
base
thumb
finger
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English (en)
French (fr)
Inventor
惠慧
王福晴
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Definitions

  • This application relates to the field of palmprint recognition technology, and in particular to a palmprint recognition method, device, computer equipment and storage medium.
  • Palm prints are the general term for all lines on the palm skin, mainly including mastoid lines, main lines and folds.
  • the palm prints are unique, that is, the palm prints of different people are very different, and no two palms are exactly the same. Based on this feature of palmprints, identification can be performed.
  • palmprint recognition includes contact palmprint recognition and non-contact palmprint recognition, both of which are used for identity recognition by collecting palmprint images.
  • non-contact palmprint recognition has become a hotspot in palmprint recognition research because of its convenient and simple operation, clean and sanitary advantages.
  • the inventor found that the palmprint feature is unstable due to the deformation of the thumb during non-contact palmprint recognition, which reduces the accuracy of palmprint recognition.
  • the embodiments of the present application provide a palmprint recognition method, device, computer equipment, and storage medium, which can improve the accuracy of palmprint recognition.
  • An embodiment of the application provides a palmprint recognition method, including: acquiring a hand image to be recognized; determining a palm area in the hand image according to a pre-trained convolutional neural network model; and according to the characteristics of the palm area , Determine the thumb root area in the hand image; compare the palm area and the thumb root area to determine the overlap between the palm area and the thumb root area; The overlapping part is cut out from the palm area; palmprint recognition is performed on the palm area where the overlapping part is cut out.
  • An embodiment of the present application also provides a palmprint recognition device, which includes: an image acquisition module for acquiring a hand image to be recognized; a first determination module for determining a palmprint recognition device based on a pre-trained convolutional neural network model The palm area in the hand image; the second determining module is used to determine the thumb root area in the hand image according to the characteristics of the palm area; the third determining module is used to determine the palm area Perform an area comparison with the thumb root area to determine the overlap portion of the palm area and the thumb root area; a region cutting module for cutting the overlap portion from the palm area; palmprint recognition The module is used for recognizing palm prints in the palm area where the overlapping part is cut off.
  • An embodiment of the present application further provides a computer device, including a memory and a processor.
  • the memory stores computer-readable instructions.
  • the processor executes the aforementioned palm. Steps of pattern recognition method.
  • the embodiments of the present application also provide a non-volatile readable storage medium storing computer readable instructions, which when executed by one or more processors, cause the one or more processors to execute the aforementioned palm Steps of pattern recognition method.
  • the palmprint recognition method, device, computer equipment, and storage medium provided by the embodiments of the present application cut off the overlap between the palm area and the thumb root area before recognizing the palm area, and then perform the resection on the palm area after the overlapped portion is removed. Palmprint recognition. Since the root area of the thumb is removed, the interference caused by the deformation of the thumb on the palmprint recognition can be reduced, the accuracy of the palmprint recognition can be improved, and the palmprint matching degree can be improved.
  • Figure 1 is a block diagram of the internal structure of a computer device in an embodiment
  • Figure 2 is a flowchart of a palmprint recognition method in an embodiment
  • Figure 3 is a schematic diagram of an image collected by an image collection module in an embodiment
  • Figure 4 is a schematic diagram of a hand image in an embodiment
  • FIG. 5 is a schematic diagram of a process of determining the palm area in the hand image in an embodiment
  • Figure 6 is a schematic diagram of a hand image in an embodiment
  • Figure 7 is a schematic diagram of a hand image in an embodiment
  • Figure 8 is a schematic diagram of a hand image in an embodiment
  • FIG. 9 is a schematic diagram of the process of recognizing palmprints on the palm area where the overlap portion is cut in an embodiment
  • Fig. 10 is a schematic structural diagram of a palmprint recognition device in an embodiment.
  • Figure 1 is a schematic structural diagram of a computer device in an embodiment of the application.
  • the computer device includes a processor, a non-volatile storage medium, a memory, and a network interface connected through a system bus.
  • the non-volatile storage medium of the computer device stores an operating system, a database, and computer-readable instructions.
  • the database may store control information sequences.
  • the processor can realize a A palmprint recognition method.
  • the processor of the computer equipment is used to provide calculation and control capabilities, and supports the operation of the entire computer equipment.
  • a computer readable instruction may be stored in the memory of the computer device.
  • the processor may execute a palmprint recognition method.
  • the network interface of the computer device is used to connect and communicate with the terminal.
  • FIG. 1 is only a block diagram of part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
  • a palmprint recognition method is proposed, which can be applied to the computer device shown in FIG. 1.
  • the palmprint recognition method provided in this embodiment, and there are also many specific forms of computer equipment.
  • a company uses a non-contact palmprint recognition access control device, and employees of this company need to perform identification on the access control device when commuting.
  • the access control device can use the palmprint recognition method provided in this embodiment to perform palmprint recognition, thereby realizing identity recognition.
  • the palmprint recognition method provided in this embodiment may specifically include the following steps:
  • S21 Obtain a hand image to be recognized; there are many triggering methods for this step, for example, when people need to perform identity recognition, trigger a button on a computer device, or perform a certain gesture operation to trigger an image acquisition module (for example, , Camera) for image acquisition.
  • an image acquisition module for example, , Camera
  • the processor in the computer device can detect the images collected by the image collection module.
  • the image acquisition module can be a part of the computer equipment, or can be set independently of the computer equipment.
  • the processor in the computer device may specifically use a target detection algorithm (Single Shot MultiBox Detector, SSD for short) to detect the image collected by the image acquisition module, locate the area where the hand is, and then obtain the hand image.
  • a target detection algorithm Single Shot MultiBox Detector, SSD for short
  • the area 32 of the hand is detected in the image 31 collected by the image acquisition module, and then the area 32 of the hand is used as the hand image, which can reduce the interference of other areas on palmprint recognition.
  • the target detection algorithm is used to facilitate the detection of complete hand images, and incomplete hand images will be filtered out, so as to ensure that the hand image contains fingertips, finger roots and palms.
  • S22 Determine the palm area in the hand image according to the pre-trained convolutional neural network model; it is understandable that the palm area is the area in the hand image that mainly reflects palmprint information. It is understandable that the root area of the thumb is the area where the mark 41 in FIG. 4 is located. In practical applications, there are many ways to determine the palm area in the hand image, one of which is described below with reference to FIG. 5:
  • S51 Recognizing the hand image using a pre-trained convolutional neural network model to obtain the position of the base of the index finger, the position of the base of the little finger, and the position of the fingertip of any finger other than the thumb;
  • the convolutional neural network model is trained by a training data set including a number of hand images that have marked the position of the base of the index finger, the position of the base of the little finger, and the position of the fingertip of any finger other than the thumb;
  • the position of the fingertip of any finger other than the thumb can be the position of the index finger, the middle finger, the ring finger, or the little finger.
  • a pre-trained convolutional neural network model is used to recognize the hand image to obtain the position of the base of the index finger, the position of the base of the little finger, and the position of the fingertip of any finger other than the thumb .
  • the Cascade convolutional neural network model is trained.
  • the training data set includes a number of index finger root positions, pinky root positions and middle finger fingertip positions that have been marked. Hand image.
  • the Cascade convolutional neural network model is used to extract the key points of the newly input hand image, and the position of the root of the index finger, the root of the little finger, and the position of the middle finger can be obtained.
  • S52 Determine the palm area according to the position of the base of the index finger, the position of the base of the little finger, and the position of the fingertip.
  • it can be set according to the shape of the palm area as required, for example, it can be set to any shape such as a circle, an ellipse, a square, and a rectangle.
  • the palm area is set as a square area 61 with the line AB between the base of the index finger A and the base of the little finger B as one side, and the square area 61
  • the center of is located on the side of the line away from the fingertip position D. Since the line AB between the base position of the index finger A and the base position B of the little finger can divide the palm area into two parts: one part is mainly the area where the four fingers other than the thumb are located, and this area is The line AB is close to the fingertip position D; the other part is mainly the area where the thumb and palm are located, and this area is the side of the line AB away from the fingertip position D.
  • the above-mentioned square area 61 defines the approximate orientation of one side and the center, so that the only square area can be determined as the palm area.
  • Steps S51 and S52 provide a relatively simple method for determining the palm area.
  • the following methods can also be used to determine the palm area: First, extract the position of the base of the index finger and the base of the little finger in the hand image according to S51 And middle finger fingertip position D.
  • the connection AB rotates to the horizontal direction, and the finger is up. Then, determine the center and one edge of the palm area, and then determine the palm area.
  • S23 Determine the root area of the thumb in the hand image according to the characteristics of the palm area; in practical applications, the shape of the root area of the thumb can be set as needed, for example, it can be set to a circle or an ellipse. Shape, square, rectangle and other arbitrary shapes.
  • the hand image is selected to meet the preset conditions
  • the ellipse area is taken as the thumb root area; wherein the preset condition includes: the length of the long axis of the ellipse area is (1 ⁇ 10% of the length of the line AB )*2/5, the length of the minor axis is (1 ⁇ 10%)*1/4 of the length of the line AB, the center F of the elliptical area is located on the first side AG of the square area, so The first side AG is a side perpendicular to the line AB and close to the thumb.
  • the center F of the elliptical area is related to whether the hand in the hand image is the left hand or the right hand. If it is a left hand, the left side of the palm area is close to the thumb, and the center F of the ellipse area can be set on the left side of the square x-shaped palm area. If it is a right hand, the right side of the palm area is close to the thumb. Set the center F of the ellipse area on the right side of the square palm area.
  • a method for judging whether the hand on the hand image is the left hand or the right hand is introduced: to determine whether the ordinate of the middle finger tip position is greater than the ordinate of the little finger base position. Coordinates, and determine whether the abscissa of the middle finger tip position is smaller than the abscissa of the little finger root position:
  • the position of the tip of the middle finger is smaller than the position of the base of the little finger, and the abscissa of the position of the middle finger tip is smaller than the position of the base of the little finger, it is the right hand.
  • the up and down direction of the hand can be known by judging the size relationship between the ordinate of the fingertip position of the middle finger and the ordinate of the base of the little finger.
  • the thumb is on the left or right of the palm.
  • the up and down direction of the hand and the position of the thumb relative to the palm it can be determined whether the hand in the hand image is the left hand or the right hand.
  • the hand in the hand image is the left hand or the right hand, it can be determined which side of the square palm area the center of the ellipse area is set, and the abscissa of the center of the ellipse area can be determined.
  • the side of the palm area where the center of the ellipse is located is the first side, and the specific position of the center of the ellipse on the first side can be determined according to the side length of the square.
  • the ordinate of the center of the ellipse is (1 ⁇ 10%)*4/5 of the side length of the square.
  • the 4/5 here is an empirical value, which can be modified according to the actual application.
  • S24 Determine the overlap between the palm area and the thumb root area by comparing the palm area with the thumb root area; for example, as shown in FIG. 8, the square area is the palm Area, the ellipse area is the thumb root area, and the overlap of the square area and the ellipse area is the shaded part in Figure 8.
  • the palm area and the thumb area are compared.
  • the specific comparison method may include: determining the coordinates of each pixel in the palm area and the coordinates of each pixel in the thumb root area, and comparing the coordinates in the palm area and the thumb root area
  • the area composed of the same pixels is the above overlapping part.
  • S91 Determine the cosine similarity between the feature vector of the palm area where the overlap is removed and the feature vectors of multiple palmprint images stored in advance; it is understandable that the feature vector is the palm area where the overlap is removed A vector composed of multiple palmprint features of.
  • a machine learning model for example, a lightweight convolutional neural network MobileNet
  • the cosine similarity is to evaluate the similarity of the two feature vectors by calculating the cosine value of the included angle of the two feature vectors, and then to know the difference between the palm area where the overlap is removed and the pre-stored palmprint image The similarity.
  • S92 Determine, according to the cosine similarity, whether the palm area where the overlapping part is cut is successfully recognized. For example, if the cosine similarity between the feature vector of the palm area where the overlap is removed and the feature vector of a palmprint image stored in advance is higher than a preset threshold, it is considered that the palm area where the overlap is removed is similar to the pre-stored palmprint image. This palmprint image is sufficiently similar that the two match successfully, that is, the palm area where the overlapped part is cut is successfully recognized.
  • the use of cosine similarity to evaluate the similarity between the palm region where the overlapped portion is cut off and the pre-stored palmprint image has the advantages of simple calculation and easy implementation.
  • other indicators can also be used to evaluate the similarity between the two.
  • the palmprint recognition method provided in this embodiment, before recognizing the palm area, the overlapping part with the thumb root area is removed, and then the palmprint recognition is performed on the palm area after the overlapping part is removed. Since the root area of the thumb is removed, the interference caused by the deformation of the thumb on the palmprint recognition can be reduced, the accuracy of the palmprint recognition can be improved, and the palmprint matching degree can be improved.
  • a palmprint recognition device 100 is provided.
  • the palmprint recognition device 100 may be integrated into the above-mentioned computer equipment, and may specifically include:
  • the image acquisition module 101 is used to acquire a hand image to be recognized
  • the first determining module 102 is configured to determine the palm area in the hand image according to the pre-trained convolutional neural network model
  • the second determining module 103 is configured to determine the thumb root area in the hand image according to the characteristics of the palm area;
  • the third determining module 104 is configured to determine the overlapping portion of the palm area and the thumb root area by comparing the palm area and the thumb root area;
  • the area cutting module 105 is used to cut the overlapped part from the palm area
  • the palmprint recognition module 106 is used for recognizing palmprints on the palm area where the overlapped part is cut.
  • the first determining module 102 includes: a first determining unit for recognizing the hand image using a pre-trained convolutional neural network model to obtain the position of the base of the index finger and the little finger The root position and the fingertip position of any finger other than the thumb; the convolutional neural network model consists of a number of marked index finger root positions, little finger root positions and any one except the thumb The training data set of the hand image of the fingertip position of the finger is obtained through training; the second determining unit is configured to determine the palm area according to the position of the base of the index finger, the position of the base of the little finger, and the position of the fingertip.
  • the palm area is a square area with a line between the base of the index finger and the base of the little finger as one side, and the center of the square area is located on the line The side away from the fingertip position.
  • the second determining module 103 further includes: a region selecting unit, configured to select an elliptical region that meets a preset condition in the hand image, and use the elliptical region as the thumb root region;
  • the preset conditions include: the length of the major axis of the elliptical area is (1 ⁇ 10%)*2/5 of the length of the line, and the length of the minor axis is (() of the length of the line 1 ⁇ 10%)*1/4, the center of the elliptical area is located on the first side of the square area, and the first side is the side perpendicular to the line and close to the thumb.
  • the position of the center of the elliptical area on the first side is determined according to the side length of the square area.
  • the ordinate of the center of the elliptical area is (1 ⁇ 10%)*4/5 of the side length of the square area.
  • the palmprint recognition module 106 is specifically configured to: determine the cosine similarity between the feature vectors of the palm region where the overlapped portion is removed and the feature vectors of multiple prestored palmprint images; The cosine similarity determines whether the palm area where the overlap portion is cut is successfully recognized.
  • the area cutting module cuts off the overlap between the palm area and the thumb root area, and then the palmprint recognition module cuts off the overlapped part. Recognize palm prints in the palm area. Since the root area of the thumb is removed, the interference caused by the deformation of the thumb on the palmprint recognition can be reduced, the accuracy of the palmprint recognition can be improved, and the palmprint matching degree can be improved.
  • a computer device includes a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor, and the processor executes all
  • the computer-readable instructions implement the following steps: obtain the hand image to be recognized; determine the palm area in the hand image according to the pre-trained convolutional neural network model; determine the palm area according to the characteristics of the palm area The thumb root area in the hand image; by comparing the palm area with the thumb root area, determine the overlap between the palm area and the thumb root area; and change the overlap from Cutting out the palm area; and performing palm print recognition on the palm area where the overlapping part is cut out.
  • the determining the palm area in the hand image according to a pre-trained convolutional neural network model executed by the processor includes: using a pre-trained convolutional neural network model to Recognizing the hand image, the position of the base of the index finger, the position of the base of the little finger, and the position of the fingertip of any finger other than the thumb are obtained; the convolutional neural network model is marked by including several The index finger root position, the little finger root position, and the fingertip position of any finger other than the thumb are trained on the training data set of hand images; according to the index finger root position, the little finger root position and the position The fingertip position determines the palm area.
  • the palm area is a square area with a line between the base of the index finger and the base of the little finger as one side, and the center of the square area is located on the line The side away from the fingertip position.
  • the step of determining the root region of the thumb in the hand image performed by the processor includes: selecting an elliptical region that meets a preset condition in the hand image, and combining the ellipse The area is taken as the root area of the thumb; wherein the preset conditions include: the length of the long axis of the elliptical area is (1 ⁇ 10%)*2/5 of the length of the line, and the length of the short axis Is (1 ⁇ 10%)*1/4 of the length of the line, the center of the elliptical area is located on the first side of the square area, and the first side is perpendicular to the line and close to The side of the thumb.
  • the position of the center of the elliptical area on the first side is determined according to the side length of the square area. In some embodiments, the ordinate of the center of the elliptical area is (1 ⁇ 10%)*4/5 of the side length of the square area.
  • the step of recognizing palm prints on the palm area from which the overlapped portion is cut off by the processor includes: determining that the feature vector of the palm area from which the overlap portion is cut off is different from a plurality of pre-stored palms. The cosine similarity between the feature vectors of the pattern image; according to the cosine similarity, it is determined whether the palm area where the overlapped part is cut off is successfully recognized.
  • a non-volatile readable storage medium storing computer readable instructions.
  • the one or more processors execute the following Steps: Obtain the hand image to be recognized; determine the palm area in the hand image according to the pre-trained convolutional neural network model; determine the thumb root in the hand image according to the characteristics of the palm area Area; by comparing the palm area and the thumb root area to determine the overlap of the palm area and the thumb root area; cut the overlap from the palm area; The palm area of the overlapping part is cut out for palmprint recognition.
  • the determination of the palm area in the hand image based on the pre-trained convolutional neural network model executed by one or more processors includes: using the pre-trained convolutional neural network model to perform The hand image is identified to obtain the position of the base of the index finger, the position of the base of the little finger, and the position of the fingertip of any finger other than the thumb; the convolutional neural network model consists of a number of labeled
  • the training data set of hand images of the position of the base of the index finger, the position of the base of the little finger, and the position of the fingertip of any finger other than the thumb is obtained by training; according to the position of the base of the index finger, the position of the base of the little finger, and The fingertip position determines the palm area.
  • the palm area is a square area with a line between the base of the index finger and the base of the little finger as one side, and the center of the square area is located on the line The side away from the fingertip position.
  • the step performed by one or more processors to determine the thumb root region in the hand image includes: selecting an elliptical region that meets a preset condition in the hand image, and combining the An elliptical area is used as the root area of the thumb; wherein the preset conditions include: the length of the long axis of the elliptical area is (1 ⁇ 10%)*2/5 of the length of the connecting line, and the short axis is The length is (1 ⁇ 10%)*1/4 of the length of the line, the center of the elliptical area is located on the first side of the square area, and the first side is perpendicular to the line and Near the side of the thumb.
  • the position of the center of the elliptical area on the first side is determined according to the side length of the square area. In some embodiments, the ordinate of the center of the elliptical area is (1 ⁇ 10%)*4/5 of the side length of the square area.
  • the steps performed by one or more processors to perform palmprint recognition on the palm region from which the overlapping part is cut off include: determining that the feature vector of the palm region from which the overlapping part is cut off is different from a plurality of pre-stored The cosine similarity between the feature vectors of the palmprint images; according to the cosine similarity, it is determined whether the palm area where the overlapped part is cut is successfully recognized.
  • the beneficial effects of the non-volatile readable storage medium provided in the present application are the same as those of the palmprint recognition method and device described above, and will not be repeated here.
  • the computer program can be stored in a computer readable storage medium. When executed, it may include the processes of the above-mentioned method embodiments.
  • the aforementioned storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disc, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM), etc.

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Abstract

一种掌纹识别方法、装置、计算机设备和存储介质，所述方法包括：获取待识别的手部图像（S21）；根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域（S22）；根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域（S23）；通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分（S24）；将所述重叠部分从所述手掌区域中切除（S25）；对切除所述重叠部分的手掌区域进行掌纹识别（S26）。由于切除了大拇指根部区域，因此可以减少因大拇指形变对掌纹识别造成的干扰，能够提高掌纹识别的准确性，提高掌纹匹配度。

Description

掌纹识别方法、装置、计算机设备和存储介质
本申请要求与2019年2月20日提交中国专利局、申请号为2019101271501、申请名称为“掌纹识别方法、装置、计算机设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及掌纹识别技术领域，特别是涉及一种掌纹识别方法、装置、计算机设备和存储介质。
背景技术
掌纹是手掌皮肤上所有纹路的统称，主要包括乳突纹、主线和皱褶。掌纹具有唯一性，即不同的人的掌纹千差万别，没有任何两个手掌是完全相同的。基于掌纹的这一特点，可以进行身份鉴别。
目前，掌纹识别有接触式掌纹识别和非接触式掌纹识别，这两种都是通过采集掌纹的图像来进行身份识别。其中的非接触式掌纹识别因具有操作方便简单、干净卫生等优势成为了掌纹识别研究的一个热点。在对相关技术研究过程中，发明人发现：在进行非接触掌纹识别时因为大拇指的形变使得掌纹特征不稳定，所以会降低掌纹识别的准确度。
发明内容
本申请实施例提供一种掌纹识别方法、装置、计算机设备和存储介质，能够提高掌纹识别的准确性。
本申请实施例提供一种掌纹识别方法，包括：获取待识别的手部图像；根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；将所述重叠部分从所述手掌区域中切除；对切除所述重叠部分的手掌区域进行掌纹识别。
本申请实施例还提供一种掌纹识别装置，该装置包括：图像获取模块，用于获取待识别的手部图像；第一确定模块，用于根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；第二确定模块，用于根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；第三确定模块，用于通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；区域切除模块，用于将所述重叠部分从所述手掌区域中切除；掌纹识别模块，用于对切除所述重叠部分的手掌区域进行掌纹识别。
本申请实施例还提供一种计算机设备，包括存储器和处理器，所述存储器中存储有计算机可读指令，所述计算机可读指令被所述处理器执行时，使得所述处理器执行上述掌纹识别方法的步骤。
本申请实施例还提供一种存储有计算机可读指令的非易失性可读存储介质，所述计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行上述掌纹识别方法的步骤。
本申请实施例提供的掌纹识别方法、装置、计算机设备和存储介质，在对手掌区域进行识别之前，将其与大拇指根部区域的重叠部分切除，然后对切除重叠部分切除后的手掌区域进行掌纹识别。由于切除了大拇指根部区域，因此可以减少因大拇指形变对掌纹识别造成的干扰，能够提高掌纹识别的准确性，提高掌纹匹配度。
附图说明
图1为一个实施例中计算机设备的内部结构框图；
图2为一个实施例中掌纹识别方法的流程图；
图3为一个实施例中图像采集模块采集到的图像的示意图；
图4为一个实施例中手部图像的示意图；
图5为一个实施例中确定所述手部图像中的手掌区域的流程示意图；
图6为一个实施例中手部图像的示意图；
图7为一个实施例中手部图像的示意图；
图8为一个实施例中手部图像的示意图；
图9为一个实施例中对切除所述重叠部分的手掌区域进行掌纹识别的流程示意图；
图10为一个实施例中掌纹识别装置的结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。可以理解， 本申请所使用的术语“第一”、“第二”等可在本文中用于描述各种元件，但这些元件不受这些术语限制。这些术语仅用于将第一个元件与另一个元件区分。
图1为本申请一个实施例中计算机设备的结构示意图。如图1所示，该计算机设备包括通过系统总线连接的处理器、非易失性存储介质、存储器和网络接口。其中，该计算机设备的非易失性存储介质存储有操作系统、数据库和计算机可读指令，数据库中可存储有控件信息序列，该计算机可读指令被处理器执行时，可使得处理器实现一种掌纹识别方法。该计算机设备的处理器用于提供计算和控制能力，支撑整个计算机设备的运行。该计算机设备的存储器中可存储有计算机可读指令，该计算机可读指令被处理器执行时，可使得处理器执行一种掌纹识别方法。该计算机设备的网络接口用于与终端连接通信。本领域技术人员可以理解，图1中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提出了一种掌纹识别方法，该掌纹识别方法可以应用于图1所示出的计算机设备中。本实施例提供的掌纹识别方法的具体应用场景有多种，计算机设备的具体形式也有多种。例如，某公司采用的是非接触式掌纹识别的门禁设备，该公司的员工在上下班时需要在门禁设备上进行身份识别。此时，门禁设备作为一种计算机设备可以采用本实施例提供的掌纹识别方法进行掌纹识别，进而实现身份识别。参考图2，本实施例提供的掌纹识别方法具体可以包括以下步骤：
S21、获取待识别的手部图像；该步骤的触发方式有多种，例如，当人们需要进行身份识别时，触发计算机设备上的按键，或者进行某种手势操作，以触发图像采集模块(例如，摄像头)进行图像采集。这样，计算机设备中的处理器可以对图像采集模块采集到的图像进行检测。其中，图像采集模块可以为计算机设备的一部分，也可以独立于计算机设备而设置。
在实际中，计算机设备中的处理器具体可以采用目标检测算法(Single Shot MultiBox Detector，简称SSD)对图像采集模块采集的图像进行检测，定位手部所在区域，进而得到手部图像。例如，在如图3所示，在图像采集模块采集的图像31中检测到手部所在区域32，进而将手部所在区域32作为手部图像，这样可以减少其他区域对掌纹识别造成干扰，同时，采用目标检测算法便于检测出完整的手部图像，不完整的手部图像会被过滤掉，从而保证手部图像包含指尖、指根和手掌。
S22、根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；可理解的是，手掌区域是手部图像中主要体现掌纹信息的区域。可理解的是，大拇指根部区域为图4中标记41所在的区域。在实际应用中，确定所述手部图像中手掌区域的方式有多种，下面参考图5介绍其中一种：
S51、采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置；所述卷积神经网络模型由包括若干张已标记出食指指根位置、小指指根位置和除大拇指之外的任意一手指的指尖位置的手部图像的训练数据集训练得到；
可理解的是，上述除大拇指之外的任意一手指的指尖位置，可以为食指指尖位置、中指指尖位置、无名指指尖位置或小指指尖位置。本步骤中采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置。以中指指尖位置为例，对其具体识别过程进行说明：对Cascade卷积神经网络模型进行训练，训练数据集中包括若干张已经标注过食指指根位置、小指指根位置和中指指尖位置的手部图像。在对Cascade卷积神经网络模型训练完成之后，利用该模型对新输入的手部图像进行关键点提取，便可以得到食指指根位置、小指指根位置和中指指尖位置。
S52、根据所述食指指根位置、所述小指指根位置和所述指尖位置，确定所述手掌区域。在实际应用中，可以根据需要对手掌区域的形状进行设置，例如，可以设置成圆形、椭圆形、正方形、长方形等任意形状。
例如，如图6所示，将手掌区域设置为一个以所述食指指根位置A和所述小指指根位置B之间的连线AB为一条边的正方形区域61，且所述正方形区域61的中心位于所述连线远离所述指尖位置D的一侧。由于所述食指指根位置A和所述小指指根位置B之间的连线AB可以把手掌区域分割为两部分：一部分主要是除大拇指之外的四个手指所在的区域，该区域为上述连线AB靠近上述指尖位置D的一侧；另一部分主要是大拇指及手掌所在的区域，该区域为上述连线AB远离上述指尖位置D的一侧。上述正方形区域61对 其一条边和中心所在大致方位进行限定，从而可以确定唯一的一个正方形区域作为手掌区域。
步骤S51和S52提供了一种比较简单的确定手掌区域的方法，当然，也可以采用下面的方法确定手掌区域：首先，依据S51提取出手部图像中的食指指根位置A、小指指根位置B和中指指尖位置D。为方便计算，可根据食指指根位置A和小指指根位置B的连线AB、连线AB与中指指尖位置D的上下关系，确定手的倾斜角度，根据倾斜角度对手部图像进行旋转，直至连线AB旋转到水平方向，且手指向上。然后，确定手掌区域的中心和一条边，进而确定手掌区域。具体为：如图7所示，设置连线AB的中垂线CE，E点位于连线AB的中点，且中垂线CE的长度length为连线AB的长度的一半，将点C作为正方形区域的中点，且以连线AB作为正方形区域的一条边，形成一个正方形区域，将该正方形区域作为手掌区域。该手掌区域的四个顶点分别为p1(C.x-length,C.y-length)、p2(C.x+length,C.y-length)、p3(C.x-length,C.y+length)和p4(C.x+length,C.y+length)。
S23、根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；在实际应用中，可以根据需要对大拇指根部区域的形状进行设置，例如，可以设置成圆形、椭圆形、正方形、长方形等任意形状。下面以大拇指根部区域为椭圆形为例，对确定所述手部图像中的大拇指根部区域的一种方式进行介绍：如图8所示，在所述手部图像中选取满足预设条件的椭圆区域，并将所述椭圆区域作为所述大拇指根部区域；其中，所述预设条件包括：所述椭圆区域的长轴的长度为所述连线AB的长度的(1±10％)*2/5，短轴的长度为所述连线AB的长度的(1±10％)*1/4，所述椭圆区域的中心F位于所述正方形区域的第一边AG上，所述第一边AG为垂直于所述连线AB且靠近大拇指的一边。也就是说，将椭圆区域的长轴设置为连线AB的长度的(1±10％)*2/5，将椭圆区域的短轴设置为连线AB的长度的(1±10％)*1/4，即确定了椭圆区域的长轴和短轴。椭圆区域的中心F与手部图像中的手是左手还是右手有关。如果是左手，手掌区域左侧的边靠近大拇指，可以将椭圆区域的中心F设置在正方x形的手掌区域左侧的边上，如果是右手，手掌区域右侧的边靠近大拇指，可以将椭圆区域的中心F设置在正方形的手掌区域右侧的边上。
以没有依据手的倾斜角度进行旋转调整的手部图像为例，介绍一种判断手部图像上的手是左手还是右手的方式：判断中指指尖位置的纵坐标是否大于小指指根位置的纵坐标，并判断中指指尖位置的横坐标是否小于小指指根位置的横坐标：
若中指指尖位置大于小指指根位置，且中指指尖位置的横坐标小于小指指根位置的横坐标，则为左手；
若中指指尖位置大于小指指根位置，且中指指尖位置的横坐标大于小指指根位置的横坐标，则为右手；
若中指指尖位置小于小指指根位置，且中指指尖位置的横坐标大于小指指根位置的横坐标，则为左手；
若中指指尖位置小于小指指根位置，且中指指尖位置的横坐标小于小指指根位置的横坐标，则为右手。
可理解的是，通过判断中指指尖位置的纵坐标和小指指根位置的纵坐标的大小关系，可以知道手的上下方向。通过判断中指指尖位置的横坐标和小指指根位置的横坐标的大小关系，可以知道大拇指在手掌的左侧还是右侧。通过手的上下方向以及大拇指相对手掌的方位，可以明确手部图像中的手是左手还是右手。当明确手部图像中的手是左手还是右手后，便可以确定椭圆区域的中心设置在正方形的手掌区域的哪条边上了，便可以确定椭圆区域的中心的横坐标。
在上文中，椭圆区域的中心所在的手掌区域的边为第一边，椭圆中心在第一边上的具体位置可以根据正方形的边长确定。例如，椭圆中心的纵坐标为正方形的边长的(1±10％)*4/5。这里的4/5为经验值，在实际应用中可以根据情况修改。
S24、通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；举例来说，如图8所示，正方形区域为手掌区域，椭圆区域为大拇指根部区域，正方形区域和椭圆区域的重叠部分即图8中的阴影部分。这里，将手掌区域和大拇指区域进行区域比对，具体比对方式可以包括：确定手掌区域中各个像素的坐标和大拇指根部区域中各个像素的坐标，将手掌区域和大拇指根部区域中坐标相同的各个像素组成的区域即为上述重叠部分。
S25、将所述重叠部分从所述手掌区域中切除；将重叠部分从手掌区域中切除的方式有多种，例如，将重叠部分的像素均置为0。
S26、对切除所述重叠部分的手掌区域进行掌纹识别。在实际应用中，对切除所述重叠部分的手掌区域进行掌纹识别的方式有多种，下面参考图9介绍其中一种：
S91、确定切除所述重叠部分的手掌区域的特征向量分别与预先存储的多个掌纹图像的特征向量之间 的余弦相似度；可理解的是，特征向量为切除所述重叠部分的手掌区域的多个掌纹特征所组成的向量。具体可以采用机器学习模型(例如，轻量化卷积神经网络MobileNet)从手掌区域中提取特征向量。可理解的是，余弦相似度是通过计算两个特征向量的夹角余弦值来评估两个特征向量的相似度，进而得知切除所述重叠部分的手掌区域与预先存储的掌纹图像之间的相似度。
S92、根据所述余弦相似度，确定所述切除所述重叠部分的手掌区域是否识别成功。例如，切除所述重叠部分的手掌区域的特征向量与预先存储的一个掌纹图像的特征向量之间的余弦相似度高于预设阈值，则认为切除所述重叠部分的手掌区域与预先存储的这个掌纹图像足够相似，两者匹配成功，即切除所述重叠部分的手掌区域识别成功。
这里，采用余弦相似度评价切除所述重叠部分的手掌区域与预先存储的掌纹图像的相似度，具有计算简单、易实现的优点。当然，还可以采用其他的指标来评价两者的相似度。本实施例提供的掌纹识别方法，在对手掌区域进行识别之前，将其与大拇指根部区域的重叠部分切除，然后对切除重叠部分切除后的手掌区域进行掌纹识别。由于切除了大拇指根部区域，因此可以减少因大拇指形变对掌纹识别造成的干扰，能够提高掌纹识别的准确性，提高掌纹匹配度。
如图10所示，在一个实施例中，提供了一种掌纹识别装置100，该掌纹识别装置100可以集成于上述的计算机设备中，具体可以包括：
图像获取模块101，用于获取待识别的手部图像；
第一确定模块102，用于根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；
第二确定模块103，用于根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；
第三确定模块104，用于通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；
区域切除模块105，用于将所述重叠部分从所述手掌区域中切除；
掌纹识别模块106，用于对切除所述重叠部分的手掌区域进行掌纹识别。
在一些实施例中，第一确定模块102包括：第一确定单元，用于采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置；所述卷积神经网络模型由包括若干张已标记出食指指根位置、小指指根位置和除大拇指之外的任意一手指的指尖位置的手部图像的训练数据集训练得到；第二确定单元，用于根据所述食指指根位置、所述小指指根位置和所述指尖位置，确定所述手掌区域。
在一些实施例中，所述手掌区域是一个以所述食指指根位置和所述小指指根位置之间的连线为一条边的正方形区域，且所述正方形区域的中心位于所述连线远离所述指尖位置的一侧。
在一些实施例中，第二确定模块103还包括：区域选取单元，用于在所述手部图像中选取满足预设条件的椭圆区域，并将所述椭圆区域作为所述大拇指根部区域；其中，所述预设条件包括：所述椭圆区域的长轴的长度为所述连线的长度的(1±10％)*2/5，短轴的长度为所述连线的长度的(1±10％)*1/4，所述椭圆区域的中心位于所述正方形区域的第一边上，所述第一边为垂直于所述连线且靠近大拇指的一边。
在一些实施例中，所述椭圆区域的中心在所述第一边上的位置根据所述正方形区域的边长确定。
在一些实施例中，所述椭圆区域的中心的纵坐标为所述正方形区域的边长的(1±10％)*4/5。
在一些实施例中，掌纹识别模块106具体用于：确定切除所述重叠部分的手掌区域的特征向量分别与预先存储的多个掌纹图像的特征向量之间的余弦相似度；根据所述余弦相似度，确定所述切除所述重叠部分的手掌区域是否识别成功。
本申请提供的掌纹识别装置，掌纹识别模块在对手掌区域进行识别之前，区域切除模块将手掌区域与大拇指根部区域的重叠部分切除，然后掌纹识别模块才对切除重叠部分切除后的手掌区域进行掌纹识别。由于切除了大拇指根部区域，因此可以减少因大拇指形变对掌纹识别造成的干扰，能够提高掌纹识别的准确性，提高掌纹匹配度。
在一些实施例中，提出了一种计算机设备，所述计算机设备包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机可读指令，所述处理器执行所述计算机可读指令时实现以下步骤：获取待识别的手部图像；根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；将所述重叠部分从所述手掌区域中切除； 对切除所述重叠部分的手掌区域进行掌纹识别。在一些实施例中，所述处理器所执行的所述根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域，包括：采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置；所述卷积神经网络模型由包括若干张已标记出食指指根位置、小指指根位置和除大拇指之外的任意一手指的指尖位置的手部图像的训练数据集训练得到；根据所述食指指根位置、所述小指指根位置和所述指尖位置，确定所述手掌区域。在一些实施例中，所述手掌区域是一个以所述食指指根位置和所述小指指根位置之间的连线为一条边的正方形区域，且所述正方形区域的中心位于所述连线远离所述指尖位置的一侧。
在一些实施例中，所述处理器所执行的确定所述手部图像中的大拇指根部区域的步骤包括：在所述手部图像中选取满足预设条件的椭圆区域，并将所述椭圆区域作为所述大拇指根部区域；其中，所述预设条件包括：所述椭圆区域的长轴的长度为所述连线的长度的(1±10％)*2/5，短轴的长度为所述连线的长度的(1±10％)*1/4，所述椭圆区域的中心位于所述正方形区域的第一边上，所述第一边为垂直于所述连线且靠近大拇指的一边。在一些实施例中，所述椭圆区域的中心在所述第一边上的位置根据所述正方形区域的边长确定。在一些实施例中，所述椭圆区域的中心的纵坐标为所述正方形区域的边长的(1±10％)*4/5。在一些实施例中，所述处理器所执行的对切除所述重叠部分的手掌区域进行掌纹识别的步骤包括：确定切除所述重叠部分的手掌区域的特征向量分别与预先存储的多个掌纹图像的特征向量之间的余弦相似度；根据所述余弦相似度，确定所述切除所述重叠部分的手掌区域是否识别成功。
本申请提供的计算机设备的有益效果与上述掌纹识别方法和装置相同，这里不再赘述。
在一个实施例中，提出了一种存储有计算机可读指令的非易失性可读存储介质，该计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行以下步骤：获取待识别的手部图像；根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；将所述重叠部分从所述手掌区域中切除；对切除所述重叠部分的手掌区域进行掌纹识别。在一些实施例中，一个或多个处理器执行的所述根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域，包括：采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置；所述卷积神经网络模型由包括若干张已标记出食指指根位置、小指指根位置和除大拇指之外的任意一手指的指尖位置的手部图像的训练数据集训练得到；根据所述食指指根位置、所述小指指根位置和所述指尖位置，确定所述手掌区域。在一些实施例中，所述手掌区域是一个以所述食指指根位置和所述小指指根位置之间的连线为一条边的正方形区域，且所述正方形区域的中心位于所述连线远离所述指尖位置的一侧。在一些实施例中，一个或多个处理器执行的步骤确定所述手部图像中的大拇指根部区域，包括：在所述手部图像中选取满足预设条件的椭圆区域，并将所述椭圆区域作为所述大拇指根部区域；其中，所述预设条件包括：所述椭圆区域的长轴的长度为所述连线的长度的(1±10％)*2/5，短轴的长度为所述连线的长度的(1±10％)*1/4，所述椭圆区域的中心位于所述正方形区域的第一边上，所述第一边为垂直于所述连线且靠近大拇指的一边。在一些实施例中，所述椭圆区域的中心在所述第一边上的位置根据所述正方形区域的边长确定。在一些实施例中，所述椭圆区域的中心的纵坐标为所述正方形区域的边长的(1±10％)*4/5。在一些实施例中，一个或多个处理器执行的步骤对切除所述重叠部分的手掌区域进行掌纹识别，包括：确定切除所述重叠部分的手掌区域的特征向量分别与预先存储的多个掌纹图像的特征向量之间的余弦相似度；根据所述余弦相似度，确定所述切除所述重叠部分的手掌区域是否识别成功。
本申请提供的非易失性可读存储介质的有益效果与上述掌纹识别方法和装置相同，这里不再赘述。本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，该计算机程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，前述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)等非易失性存储介质，或随机存储记忆体(Random Access Memory，RAM)等。
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对本申请专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种掌纹识别方法，所述方法包括：
    获取待识别的手部图像；
    根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；
    根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；
    通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；
    将所述重叠部分从所述手掌区域中切除；
    对切除所述重叠部分的手掌区域进行掌纹识别。
  2. 根据权利要求1所述的方法，所述根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域，包括：采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置；所述卷积神经网络模型由包括若干张已标记出食指指根位置、小指指根位置和除大拇指之外的任意一手指的指尖位置的手部图像的训练数据集训练得到；根据所述食指指根位置、所述小指指根位置和所述指尖位置，确定所述手掌区域。
  3. 根据权利要求2所述的方法，所述手掌区域是一个以所述食指指根位置和所述小指指根位置之间的连线为一条边的正方形区域，且所述正方形区域的中心位于所述连线远离所述指尖位置的一侧。
  4. 根据权利要求3所述的方法，所述确定所述手部图像中的大拇指根部区域，包括：
    在所述手部图像中选取满足预设条件的椭圆区域，并将所述椭圆区域作为所述大拇指根部区域；其中，所述预设条件包括：所述椭圆区域的长轴的长度为所述连线的长度的(1±10％)*2/5，短轴的长度为所述连线的长度的(1±10％)*1/4，所述椭圆区域的中心位于所述正方形区域的第一边上，所述第一边为垂直于所述连线且靠近大拇指的一边。
  5. 根据权利要求4所述的方法，所述椭圆区域的中心在所述第一边上的位置根据所述正方形区域的边长确定。
  6. 根据权利要求4所述的方法，所述椭圆区域的中心的纵坐标为所述正方形区域的边长的(1±10％)*4/5。
  7. 根据权利要求1所述的方法，对切除所述重叠部分的手掌区域进行掌纹识别，包括：确定切除所述重叠部分的手掌区域的特征向量分别与预先存储的多个掌纹图像的特征向量之间的余弦相似度；根据所述余弦相似度，确定所述切除所述重叠部分的手掌区域是否识别成功。
  8. 一种掌纹识别装置，所述装置包括：
    图像获取模块，用于获取待识别的手部图像；
    第一确定模块，用于根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；
    第二确定模块，用于根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；
    第三确定模块，用于通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；
    区域切除模块，用于将所述重叠部分从所述手掌区域中切除；
    掌纹识别模块，用于对切除所述重叠部分的手掌区域进行掌纹识别。
  9. 根据权利要求8所述的装置，所述第一确定模块包括：
    第一确定单元，用于采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置；所述卷积神经网络模型由包括若干张已标记出食指指根位置、小指指根位置和除大拇指之外的任意一手指的指尖位置的手部图像的训练数据集训练得到；
    第二确定单元，用于根据所述食指指根位置、所述小指指根位置和所述指尖位置，确定所述手掌区域。
  10. 根据权利要求9所述的装置，所述手掌区域是一个以所述食指指根位置和所述小指指根位置之间的连线为一条边的正方形区域，且所述正方形区域的中心位于所述连线远离所述指尖位置的一侧。
  11. 根据权利要求10所述的装置，所述第二确定模块还包括：
    区域选取单元，用于在所述手部图像中选取满足预设条件的椭圆区域，并将所述椭圆区域作为所述大拇指根部区域；其中，所述预设条件包括：所述椭圆区域的长轴的长度为所述连线的长度的(1±10％)*2/5，短轴的长度为所述连线的长度的(1±10％)*1/4，所述椭圆区域的中心位于所述正方形区域的第一边上，所述第一边为垂直于所述连线且靠近大拇指的一边。
  12. 根据权利要求11所述的装置，所述椭圆区域的中心在所述第一边上的位置根据所述正方形区域的边长确定。
  13. 根据权利要求11所述的装置，所述椭圆区域的中心的纵坐标为所述正方形区域的边长的(1±10％) *4/5。
  14. 根据权利要求8所述的装置，所述掌纹识别模块，具体用于确定切除所述重叠部分的手掌区域的特征向量分别与预先存储的多个掌纹图像的特征向量之间的余弦相似度；根据所述余弦相似度，确定所述切除所述重叠部分的手掌区域是否识别成功。
  15. 一种计算机设备，包括存储器和处理器，所述存储器中存储有计算机可读指令，所述计算机可读指令被所述处理器执行时，使得所述处理器执行掌纹识别方法的步骤，包括：获取待识别的手部图像；根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；将所述重叠部分从所述手掌区域中切除；对切除所述重叠部分的手掌区域进行掌纹识别。
  16. 根据权利要求15所述的计算机设备，所述计算机可读指令被所述处理器执行时，使得所述处理器执行所述根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域，包括：采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置；所述卷积神经网络模型由包括若干张已标记出食指指根位置、小指指根位置和除大拇指之外的任意一手指的指尖位置的手部图像的训练数据集训练得到；根据所述食指指根位置、所述小指指根位置和所述指尖位置，确定所述手掌区域。
  17. 根据权利要求16所述的计算机设备，所述计算机可读指令被所述处理器执行以实现所述方法时使得所述手掌区域是一个以所述食指指根位置和所述小指指根位置之间的连线为一条边的正方形区域，且所述正方形区域的中心位于所述连线远离所述指尖位置的一侧。
  18. 一种存储有计算机可读指令的非易失性可读存储介质，所述计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行掌纹识别方法的步骤，包括：获取待识别的手部图像；根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域；根据所述手掌区域的特征，确定所述手部图像中的大拇指根部区域；通过将所述手掌区域和所述大拇指根部区域进行区域比对，确定所述手掌区域和所述大拇指根部区域的重叠部分；将所述重叠部分从所述手掌区域中切除；对切除所述重叠部分的手掌区域进行掌纹识别。
  19. 根据权利要求18所述的存储介质，所述计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行所述根据预先训练的卷积神经网络模型，确定所述手部图像中的手掌区域，包括：采用预先训练的卷积神经网络模型对所述手部图像进行识别，得到所述食指指根位置、所述小指指根位置和和除大拇指之外的任意一手指的指尖位置；所述卷积神经网络模型由包括若干张已标记出食指指根位置、小指指根位置和除大拇指之外的任意一手指的指尖位置的手部图像的训练数据集训练得到；根据所述食指指根位置、所述小指指根位置和所述指尖位置，确定所述手掌区域。
  20. 根据权利要求19所述的存储介质，所述计算机可读指令被所述处理器执行以实现所述方法时使得所述手掌区域是一个以所述食指指根位置和所述小指指根位置之间的连线为一条边的正方形区域，且所述正方形区域的中心位于所述连线远离所述指尖位置的一侧。
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