WO2021218183A1 - Procédé et appareil de détection de bord de certificat, dispositif et support - Google Patents

Procédé et appareil de détection de bord de certificat, dispositif et support Download PDF

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Publication number
WO2021218183A1
WO2021218183A1 PCT/CN2020/136317 CN2020136317W WO2021218183A1 WO 2021218183 A1 WO2021218183 A1 WO 2021218183A1 CN 2020136317 W CN2020136317 W CN 2020136317W WO 2021218183 A1 WO2021218183 A1 WO 2021218183A1
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Prior art keywords
target image
edge detection
face
document
image
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PCT/CN2020/136317
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English (en)
Chinese (zh)
Inventor
黄泽浩
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平安科技（深圳）有限公司
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Publication of WO2021218183A1 publication Critical patent/WO2021218183A1/fr

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    • 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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • 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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation

Definitions

  • This application relates to the technical field of financial technology (Fintech), in particular to the edge detection method, device, equipment and medium of the document.
  • the inventor realizes that when the edge of the document is damaged, it will cause a large recognition error.
  • This application provides a document edge detection method, which includes the following steps:
  • the present application also provides a document edge detection device, which includes:
  • the request receiving module is configured to obtain the target image associated with the edge detection request of the image credential when the edge detection request of the image credential is received;
  • the face recognition module is used to input the target image into a preset face recognition model, extract the face feature points in the target image, and according to the face feature points and the feature coordinates of the face feature points , Determine the face photo in the target image;
  • a credential image extraction module for extracting a credential body image containing a face photo from the target image according to the photo information of the face photo;
  • the result output module is used to input the document body image to the preset edge detection model to obtain the card edge line segment, analyze the card edge line segment, and output the certificate edge detection result.
  • the present application also provides a document edge detection device.
  • the document edge detection device includes a memory, a processor, and a computer program corresponding to the document edge detection that is stored in the memory and can run on the processor.
  • the computer program corresponding to the document edge detection is executed by the processor, the following steps are implemented:
  • the present application also provides a computer-readable storage medium on which a computer program corresponding to the edge detection of a document is stored, and when the computer program corresponding to the edge detection of the document is executed by a processor, the following steps are implemented:
  • FIG. 1 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a first embodiment of a method for detecting the edge of an application document
  • FIG. 3 is a schematic diagram of functional modules of an embodiment of the document edge detection device of this application.
  • FIG. 1 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application.
  • the edge detection device of the example embodiment of this application may be a server device, as shown in FIG. Among them, the communication bus 1002 is used to implement connection and communication between these components.
  • the user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a magnetic disk memory.
  • the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
  • FIG. 1 does not constitute a limitation on the device, and may include more or fewer components than those shown in the figure, or a combination of certain components, or different component arrangements.
  • the memory 1005 which is a computer storage medium, may include an operating network communication module, a user interface module, and a computer program corresponding to the edge detection of the document.
  • the network interface 1004 is mainly used to connect to the back-end server and communicate with the back-end server; the user interface 1003 is mainly used to connect to the client (user side) to communicate with the client; and the processor 1001 can be used to call a computer program corresponding to the edge detection of a document stored in the memory 1005, and perform operations in the following method for detecting edge of a document.
  • Fig. 2 is a schematic flowchart of the first embodiment of the document edge detection method of this application.
  • the document edge detection method includes:
  • step S10 when a request for edge detection of an image certificate is received, a target image associated with the request for edge detection of an image certificate is acquired.
  • the document edge detection method in this embodiment is applied to document edge detection equipment in financial institutions (banking institutions, insurance institutions, securities institutions, etc.) in the financial industry.
  • the credential edge detection device receives the image credential edge detection request.
  • the triggering method of the image credential edge detection request is not specifically limited, that is, the image credential edge detection request can be actively triggered by the user, for example, the user clicks "on the display page of the credential edge detection device "Edge detection” button to actively trigger the image ID edge detection request; in addition, the image ID edge detection request can also be automatically triggered, for example, the ID edge detection device is preset: when a new ID scan image is received, the image ID is automatically triggered Edge detection request.
  • the credential edge detection device receives the image credential edge detection request, and the credential edge detection device obtains the target image associated with the image credential edge detection request.
  • the target image in this embodiment contains card information, and may also include other than the card.
  • Other information, in addition, the color and size of the target image are not specifically limited, for example, the target image may be color or black and white.
  • Step S20 Input the target image into a preset face recognition model, extract the face feature points in the target image, and determine the face feature point according to the face feature point and the feature coordinates of the face feature point The face photo in the target image.
  • the face recognition model is preset in the document edge detection device, that is, the document edge detection device uses the face image as sample data, and pre-trains according to the face image to obtain the preset face recognition model, and the document edge detection device inputs the target image
  • the target image is processed through the preset face recognition model
  • the document edge detection device extracts the facial feature points in the target image
  • the document edge detection device obtains the feature coordinates of the face feature points
  • the document edge detection device According to the facial feature points and the feature coordinates of the face feature points, the face photo in the target image is determined, that is, the document edge detection device analyzes the feature point coordinates of the face feature points according to the clustering algorithm, and obtains a cluster center ( x0, y0), then, the document edge detection device obtains a minimum bounding rectangle according to the feature coordinates of each facial feature point, and the document edge detecting device uses this bounding rectangle as the face photo in the target image.
  • Step S30 according to the photo information of the face photo, extract a document body image containing the face photo from the target image.
  • the document edge detection device extracts the document body image containing the face photo from the target image according to the photo information of the face photo, and the implementation method is not specifically limited:
  • Implementation method 1 The document edge detection equipment first reduces and enlarges the standard documents (standard documents can be ID cards, library cards, student cards or passports, etc.) to obtain the smallest number of document area, document length, width, and facial feature points
  • standard documents can be ID cards, library cards, student cards or passports, etc.
  • the circumscribed rectangle and the coordinates of the cluster center the document edge detection device saves the document edge detection device to save the document length, width and the corresponding aspect ratio of the circumscribed rectangle, the proportional relationship between the cluster center coordinates, and the proportional relationship between the cluster center and the distance of the document, the document edge
  • the detection device records the proportional relationship to generate a preset ID face ratio mapping table.
  • the ID edge detection device obtains the ID subject containing the face photo in the target image according to the face photo and the preset ID face ratio mapping table, for example, ,
  • the document edge detection equipment presets the document face ratio mapping table to record that the ratio of the face image of the library witness to the document body is 1:6, and the document edge detection equipment determines that the size of the face photo is 2cm*3cm, the document edge detection The device acquires an area of 4cm*9cm as the main body image of the document according to the ratio mapping relationship of the face of the document.
  • the document edge detection device determines the document body image according to the coordinates of the face image in the target image and the coordinate relationship between the document body, that is, the document edge detection device acquires the coordinates x1, y1, and the document edge detection
  • the equipment obtains the coordinates of the document body x2 and y2; the document edge detection equipment is based on x1 ⁇ x2, y1 ⁇ y2, and x1-(x2-x1)/a, x2+(x2-x1)/a, y1-(y2-y1)/a, y2+(y2-y1)/a, the document edge detection device obtains the document body image containing the face photo in the target image, where the value of a can be around 30 (it can also be changed in different situations .)
  • step S20 includes:
  • Step a1 obtaining photo information of the face photo, where the photo information includes location information and size information of the face photo;
  • Step a2 query a preset person ID mapping table, obtain the ID type corresponding to the location information, and determine ID size information according to the ID type and the size information of the face photo;
  • Step a3 Extract a document body image containing the face photo from the target image according to the document size information and the photo information of the face photo.
  • the document edge detection device obtains the photo information of the face photo, where the photo information includes the position information and size information of the face photo; Set the mapping table of photo location information and certificate type) to obtain the document type corresponding to the location information.
  • the document edge detection device determines the document size information according to the document type and the size information of the face photo; the document edge detection device determines the document size information according to the document size information and the face
  • the photo information of the photo is to extract the document body image containing the face photo from the target image.
  • the document body image containing the face photo is extracted from the target image, so as to input the document body image into the preset edge detection model to perform the document edge detection.
  • This embodiment Only the main body image of the document is processed in, which reduces the amount of data processing and further improves the efficiency and accuracy of edge detection.
  • Step S40 Input the main body image of the document into a preset edge detection model to obtain the edge line segment of the card, analyze the edge line segment of the card, and output the edge detection result of the document.
  • the document edge detection equipment presets the edge detection model.
  • the preset edge detection model refers to the pre-set line segment monitoring algorithm.
  • the document edge detection device inputs the main body image of the document to the preset edge detection model to obtain the card edge line segment.
  • the document edge detection equipment analyzes Card edge line segment, to determine whether the card edge line segment encloses a matrix, if the card edge line segment encloses a matrix, the document edge detection device outputs a complete document edge, if the card edge line segment does not enclose a matrix, the document edge detection device outputs a document edge incomplete .
  • the face photo in the target image is recognized, and the document body image is extracted in the reverse direction based on the face photo, so that the document body image is input to the preset edge detection model to obtain the card edge line segment, and the card edge line segment is analyzed , Output the detection result of the document edge, improve the accuracy of the document edge detection in the target image, and further improve the accuracy of the identification of the document information.
  • This embodiment is a step after step S10 in the first embodiment.
  • the difference between this embodiment and the foregoing embodiment lies in:
  • the target image does not contain a human face image, input the target image to a preset edge detection model to obtain a card edge line segment, analyze the card edge line segment, and output a document edge detection result;
  • the target image contains a face image
  • extract the face feature points in the target image and determine the face in the target image according to the face feature points and the feature coordinates of the face feature points Photo.
  • the document edge detection device inputs the target image to the preset.
  • the preset face recognition model is the same as in the first embodiment, and this embodiment will not go into details
  • the recognition result is obtained (the recognition result refers to the result of whether facial feature information is extracted), and the document edge detection device is based on
  • the recognition result determines whether the target image contains a face image; if the target image does not contain a face image, the document edge detection device inputs the target image to the preset edge detection model to obtain the card edge line segment, and the document edge detection device analyzes the card edge Line segment, output the detection result of the document edge; if the target image contains a face image, the document edge detection device extracts the facial feature points in the target image, and the document edge detection device uses the face feature points and the feature coordinates of the face feature points To determine the face photo in the target image.
  • the preset face recognition model is the same as in the first embodiment, and this embodiment will not go into details
  • the recognition result refers to the result of whether facial feature information is extracted
  • This embodiment is a step after step S10 in the first embodiment.
  • the difference between this embodiment and the foregoing embodiment lies in:
  • the inclination angle of the target image is determined according to the straight line and the direct projection, and the target image is moved backward according to the inclination angle.
  • the document edge detection device inputs the target image into the preset edge detection model.
  • the document edge detection device first detects the target image in a straight line, and the document edge detection device transforms each pixel coordinate point into a unified metric that contributes to the characteristics of the straight line.
  • a straight line is a collection of a series of discrete points in the target image.
  • the pixel coordinates P(x, y) of the image are known, and r, theta is the variable to be looked for.
  • the edge detection device of the document draws each pixel (r, theta) value
  • the document edge detection device converts the image Cartesian coordinates to the polar coordinate Hough space.
  • This transformation from point to curve is called the Hough transform of a straight line.
  • the transformation is performed by quantizing the Hough parameter space into a finite interval of values. Divide or accumulate the grid.
  • the Hough transform algorithm starts, the coordinate point P(x, y) of each pixel is converted to (r, Theta) curve points are added to the corresponding grid data points.
  • a wave crest appears it means that there is a straight line.
  • the document edge detection device determines that there is a straight line, the document edge detection device projects the straight line to obtain the projection line corresponding to the straight line.
  • the document edge detection device obtains the inclination angle of the line according to the law of cosine, and the document edge detection device rotates the target according to the inclination angle. Image to complete the angle correction of the target image.
  • the document edge detection device recognizes and rotates the target image, which improves the accuracy of recognition.
  • This embodiment is a detailed step of step S40 in the first embodiment.
  • the difference between this embodiment and the foregoing embodiment lies in:
  • the detection result of the missing corner of the card edge is output.
  • the document edge detection device inputs the main body image of the document into the preset edge detection model to obtain the edge line segment of the card.
  • the document edge detection device takes the midpoint of all the edge line segments of the card according to the discrete point classification and statistical algorithm, and performs k-nearest neighbors on all midpoints Four categories. At the same time, all points of the line segment corresponding to this midpoint belong to this cluster. Then, after the document edge detection equipment classifies each cluster, the abnormal points are removed first, and then the support vector machine is used for the second classification.
  • the document edge detection equipment counts the distance from all points of each cluster to the support vector, and the document edge detection equipment takes the cube and then divides it. Take the number of all points in this cluster.
  • the document edge detection device determines the missing corner of the card edge line segment and outputs a prompt message, and vice versa.
  • the document edge detection device inputs the main body image of the document into the preset edge detection model to obtain the edge line segment of the card.
  • the document edge detection device obtains the number of pixels contained in each edge line segment of the card, and compares the number of pixels with the preset number of points, The document edge detection device determines whether the length of the edge line segment of the card is greater than the preset number of points, where the preset number of points can be a length of 10 pixels, and if the length of the card edge line segment is less than the preset number of points, delete it.
  • the document edge detection device obtains the vertex coordinates of the remaining pixels, and then uses the k-nearest neighbor algorithm to cluster them into 4 categories to obtain the number of line segments that determine the edge line segment of the card.
  • the document edge detection device determines whether the number of line segments of the card edge line segment is greater than 4. If the number of line segments is greater than 4, the edge is considered to be incomplete, and vice versa.
  • This embodiment is a step after step S40 in the first embodiment.
  • the difference between this embodiment and the foregoing embodiment lies in:
  • the target image is classified and saved to a corresponding certificate image database.
  • the document edge detection device performs text recognition on the rectangular area enclosed by the edge line of the card to obtain the text information contained in the main image of the document.
  • the text recognition method in this embodiment is not limited.
  • the text recognition method can be OCR (Optical Character Recognition (optical character recognition)
  • the document edge detection device determines the type of the document in the target image based on the text information, and then the document edge detection device saves the target image to the corresponding document image database according to the type of the document.
  • the document edge detection device classifies and saves the target image, which can facilitate the user to find it.
  • the present application also provides a document edge detection device, the document edge detection device includes:
  • the request receiving module 10 is configured to obtain the target image associated with the edge detection request of the image credential when the edge detection request of the image credential is received;
  • the face recognition module 20 is configured to input the target image into a preset face recognition model, extract facial feature points in the target image, and based on the facial feature points and the features of the face feature points Coordinates, determine the face photo in the target image;
  • the credential image extraction module 30 is configured to extract the credential body image containing the face photo from the target image according to the photo information of the face photo;
  • the result output module 40 is used to input the document body image to a preset edge detection model to obtain the edge line segment of the card, analyze the edge line segment of the card, and output the edge detection result of the document.
  • the document edge detection device includes:
  • the line segment recognition module is configured to input the target image into a preset edge detection model, output a line segment recognition result, and determine whether there is a straight line in the target image according to the line segment recognition result;
  • the image movement module is configured to determine the inclination angle of the target image according to the straight line and the direct projection if there is a straight line in the target image, and move the target image in reverse according to the inclination angle.
  • the face recognition module 20 includes:
  • a recognition judgment unit configured to input the target image into a preset face recognition model, obtain a recognition result, and determine whether the target image contains a face image according to the recognition result;
  • the input detection unit is configured to, if the target image does not contain a face image, input the target image to a preset edge detection model to obtain a card edge line segment, analyze the card edge line segment, and output a document edge detection result;
  • the extraction and determination unit is configured to, if the target image contains a face image, extract the face feature points in the target image, and determine the face feature point according to the face feature point and the feature coordinates of the face feature point The face photo in the target image.
  • the credential image extraction module 30 includes:
  • An information acquisition unit for acquiring photo information of the face photo by an application, where the photo information includes location information and size information of the face photo;
  • the query determining unit is configured to query a preset person ID mapping table, obtain the ID type corresponding to the location information, and determine the ID size information according to the ID type and the size information of the face photo;
  • the image extracting unit is configured to extract the document body image containing the face photo from the target image according to the document size information and the photo information of the face photo.
  • the result output module 40 includes:
  • the image input unit is used to input the image of the main body of the certificate to the preset edge detection model to obtain the edge line segment of the card;
  • the classification processing unit is configured to process each of the edge line segments of the card according to a preset discrete point classification statistical algorithm to obtain the midpoint of the edge line segment of the card;
  • the delete classification unit is used to classify the midpoint into four nearest neighbors, regard the points on the edge line segment of the card corresponding to the same midpoint as a cluster, delete the abnormal points in each cluster, and perform the calculation on the remaining points in each cluster.
  • Support vector machine two classification
  • a statistical comparison unit configured to count the distances from all points in each cluster to the support vector, take the cube of the distance and divide by the number of all points in the cluster to obtain a calculation result, and compare the calculation result with a preset threshold;
  • the result output unit is configured to output the detection result of the missing corner of the card edge if the calculation result is greater than the preset threshold.
  • the result output module 40 includes:
  • the image input unit is used to input the image of the main body of the certificate to the preset edge detection model to obtain the edge line segment of the card;
  • the quantity comparison unit is used to obtain the number of pixels contained in each edge line segment of the card, and compare the number of pixels with a preset number of points;
  • the information quantity unit is used to delete noise card edge line segments whose number of pixels is less than the preset number of points, and process the remaining card edge line segments according to the preset clustering algorithm to obtain the number of line segments of the card edge line segment;
  • the result output unit is configured to output the detection result of the missing corner of the card edge if the number of line segments is greater than 4.
  • the document edge detection device further includes:
  • the text recognition module is used to perform text recognition on the rectangular area enclosed by the edge line segment of the card to obtain the text information contained in the main body image of the certificate;
  • the classification saving module is used to classify and save the target image to the corresponding credential image database according to the text information.
  • the method implemented when the document edge detection device is executed can refer to the various embodiments of the document edge detection method of this application, which will not be repeated here.
  • the document edge detection device recognizes the face photo in the target image and extracts the document body image in the reverse direction according to the face photo, thereby inputting the document body image to the preset edge detection model to obtain the edge line segment of the card.
  • the edge line segment of the card is analyzed, and the detection result of the edge of the document is output, which improves the accuracy of detecting the edge of the document in the target image, and further improves the accuracy of identification of the document information.
  • the present application also provides a computer-readable storage medium.
  • the computer-readable storage medium may be volatile or non-volatile.
  • the computer readable storage medium of the present application stores a computer program corresponding to the document edge detection, and when the computer program corresponding to the document edge detection is executed by a processor, the steps of the document edge detection method described above are implemented.

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Abstract

Procédé et appareil de détection de bord de certificat, dispositif et support. Le procédé comprend les étapes consistant à : lorsqu'une requête de détection de bord de certificat d'image est reçue, acquérir une image cible associée à la requête de détection de bord de certificat d'image (S10) ; entrer l'image cible dans un modèle de reconnaissance faciale prédéfini, extraire un point de caractéristique faciale à partir de l'image cible, et déterminer une photographie faciale dans l'image cible en fonction du point de caractéristique faciale et d'une coordonnée de caractéristique du point de caractéristique faciale (S20) ; en fonction d'informations de photographie de la photographie faciale et de l'image cible, extraire une image de corps principal de certificat qui comprend la photographie faciale (S30) ; et entrer l'image de corps principal de certificat dans un modèle de détection de bord prédéfini, obtenir un segment de ligne de bord de carte, analyser le segment de ligne de bord de carte, et délivrer un résultat de détection de bord de certificat (S40). Au moyen du procédé, la précision de détection de bord de certificat est améliorée.
PCT/CN2020/136317 2020-04-30 2020-12-15 Procédé et appareil de détection de bord de certificat, dispositif et support WO2021218183A1 (fr)

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CN111582134A (zh) * 2020-04-30 2020-08-25 平安科技（深圳）有限公司 证件边沿检测方法、装置、设备和介质
CN112883959B (zh) * 2021-01-21 2023-07-25 平安银行股份有限公司 身份证照完整性检测方法、装置、设备及存储介质
CN113221926B (zh) * 2021-06-23 2022-08-02 华南师范大学 一种基于角点优化的线段提取方法

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