CN112528986A - Image alignment method, face recognition method and related device - Google Patents
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Abstract
The invention provides an image alignment method, a face recognition method and a related device, wherein the image alignment method comprises the following steps: acquiring a first image to be aligned and a standard coordinate of a preset key point in a standard image; acquiring initial coordinates of preset key points from a first image; determining a transformation matrix of the first image according to the standard coordinate and the initial coordinate; and aligning the first images to be aligned based on the conversion matrix to obtain aligned second images. The conversion matrix is converted according to the initial coordinates of the preset key points of the first image and the standard coordinates of the preset key points in the standard image, so that the first image is not deformed in the process of converting to the aligned second image, and the accuracy of face recognition is improved.
Description
Technical Field
The invention relates to the technical field of face recognition, in particular to an image alignment method, a face recognition method and a related device.
Background
In recent years, domestic consumption needs are continuously increased, the consumption financial industry is also met with a big outbreak, and fraud risks begin to show a rising trend, so that the identity recognition and fraud risk protection of users become important links of internet financial security.
However, when face recognition is performed, the recognition accuracy is affected in the recognition process due to the diversification of the pose of the face.
Disclosure of Invention
The invention mainly solves the technical problem of providing an image alignment method, a face recognition method and a related device so as to achieve the purpose of improving the image recognition precision.
In order to solve the above technical problems, a first technical solution provided by the present invention is: provided is an image alignment method including: acquiring a first image to be aligned and a standard coordinate of a preset key point in a standard image; acquiring initial coordinates of preset key points from the first image; determining a transformation matrix of the first image according to the standard coordinates and the plurality of initial coordinates; and aligning the first images to be aligned based on the conversion matrix to obtain aligned second images.
In order to solve the above technical problems, the second technical solution provided by the present invention is: a face recognition method is provided, which comprises: acquiring a face image to be recognized and a reference face image; if the face image is not a standard image, aligning the face image to obtain an aligned face image, wherein the aligned face image is obtained by any one of the image alignment methods; judging whether the aligned face image is matched with the reference face image; and if the face images to be recognized are matched, the recognition of the face images to be recognized is successful.
In order to solve the above technical problems, a third technical solution provided by the present invention is: there is provided an image alignment apparatus including: the system comprises an acquisition module, an initial coordinate acquisition module, a transformation matrix acquisition module and a transformation module, wherein the acquisition module is used for acquiring a first image to be aligned and a standard coordinate of a preset key point in a standard image; the initial coordinate acquisition module is used for acquiring initial coordinates of preset key points from the first image; the transformation matrix acquisition module is used for determining a transformation matrix according to the standard coordinate and the initial coordinate; the conversion module is used for aligning the first image to be aligned based on the conversion matrix to obtain an aligned second image.
In order to solve the above technical problems, a fourth technical solution provided by the present invention is: provided is a face recognition apparatus including: the device comprises an acquisition module, an image alignment module and a judgment module; the acquisition module is used for acquiring a face image to be recognized and a reference face image; the image alignment module is used for aligning the face image when the face image is not a standard image to obtain an aligned face image; wherein the aligned face image is obtained by any one of the image alignment methods; the judging module is used for judging whether the aligned face image is matched with the reference face image; and when matching, determining that the face image to be recognized is successfully recognized.
In order to solve the above technical problems, a fifth technical solution provided by the present invention is: providing an intelligent system comprising: a control circuit, a processor and a memory coupled to each other, wherein the memory is used for storing program instructions for implementing the image alignment method according to any one of the above items; or the memory is used for storing program instructions for implementing the face recognition method as described in any one of the above; the processor is configured to execute the program instructions stored by the memory.
In order to solve the above technical problems, a sixth technical solution provided by the present invention is: providing a storage medium storing a program file executable to implement the image alignment method as in any one of the above; or the program file can be executed to implement any one of the above-mentioned face recognition methods.
The invention has the beneficial effects that: different from the prior art, the image alignment method provided by the invention further calculates the transformation matrix by acquiring the initial coordinates of the preset key points in the first image to be aligned and the standard coordinates of the preset key points in the standard image, and further aligns the first image based on the transformation matrix. The transformation matrix is converted according to the initial coordinates of the preset key points of the first image and the standard coordinates of the preset key points in the standard image, so that the transformation matrix can be prevented from deforming in the process of converting the first image into the aligned second image, and the accuracy of image alignment is improved.
Drawings
FIG. 1 is a schematic flow chart diagram of a first embodiment of the image alignment method of the present invention;
FIG. 2 is a schematic view of a sub-flow of step S101 in FIG. 1;
FIG. 3 is a flow chart of a first embodiment of the face recognition method of the present invention;
FIG. 4 is a schematic structural diagram of a first embodiment of an image alignment apparatus of the present invention;
FIG. 5 is a schematic structural diagram of a face recognition apparatus according to a first embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a first embodiment of the intelligent system of the present invention;
FIG. 7 is a schematic diagram of the structure of the storage medium of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The present invention will be described in detail below with reference to the accompanying drawings and examples.
Fig. 1 is a schematic flow chart of an image alignment method according to a first embodiment of the present invention.
The method comprises the following steps:
step S101: and acquiring standard coordinates of preset key points in the first image to be aligned and the standard image.
The first image in the present embodiment may be any of an object image including a human face, a landscape image, a person image, and the like. Image alignment is the transformation of images at different poses in an image into a standard format. For example, when the first image is a face image, face alignment is a process of transforming the face images in different poses into an image in a standard face coordinate system. Such as aligning a tilted face to a frontal face, etc.
The preset key points can select key points capable of representing the image position characteristics, and if the first image is a face image, the preset key points at least comprise the left eye, the right eye, the nose tip, the left mouth corner and the right mouth corner of the face.
It should be noted that the standard coordinates of the preset key points in the standard image are calculated through big data, and in an embodiment, if the human faces are different in size, such as the faces of adults and children, the standard coordinates may be converted through a relevant ratio.
In addition, the standard image is a figure that is easier to distinguish than the first image to be aligned, for example, if the first image is a face image, that is, the standard image is a front face image of a human face, the alignment process of the first image is to convert the pose of the human face in the first image into a front face pose.
For clarity of explanation, fig. 2 is a sub-flowchart of step S101 in fig. 1, and is specifically shown in fig. 2. In this embodiment, a first image is taken as a face image as an example to explain, and a process of acquiring a first image to be aligned includes:
step S1011: an image to be aligned is acquired.
After the image to be aligned is acquired, firstly, the image to be aligned is identified, and whether the image comprises a human face or not is determined.
Step S1012: and detecting the position of the face in the image to be aligned by using a face detection algorithm.
If the image comprises a face image, detecting the position of a face in the image to be aligned by using a face detection algorithm.
In other embodiments, the face position detection may also be performed by a face image scanning method, which is not limited herein.
Step S1013: and intercepting a picture with the position of the face as the first image.
And cutting the image at the position where the face image is detected to obtain a first image.
Further, the standard coordinates of the preset key points in the standard image are coordinates after the images are aligned. For example, coordinates of the left eye, the right eye, the nose tip, the left mouth corner, and the right mouth corner of a standard face are obtained on an image of 112 × 112 size having the standard face, and the coordinates of the left eye, the right eye, the nose tip, the left mouth corner, and the right mouth corner obtained at this time are standard coordinates.
Specifically, in one embodiment, the standard coordinates of the left eye are (38.29459953,51.69630051), the standard coordinates of the right eye are (65.53179932,51.50139999), the standard coordinates of the nose tip are (48.02519989,71.73660278), the standard coordinates of the left mouth corner are (33.54930115,92.3655014), and the standard coordinates of the right mouth corner are (62.72990036, 92.20410156).
Step S102: and acquiring initial coordinates of preset key points from the first image.
Specifically, the initial coordinates of the preset key points corresponding to the standard coordinate position are acquired on the intercepted first image with the face image.
Specifically, in an embodiment, the first image is detected by a face keypoint detection algorithm to obtain an initial coordinate of a left eye, an initial coordinate of a right eye, an initial coordinate of a nose tip, an initial coordinate of a left mouth corner, and an initial coordinate of a right mouth corner.
In one embodiment, in order to improve the precision, the coordinates of a plurality of points in the set range of each preset key point are respectively obtained; and calculating to obtain the average coordinate of the coordinates of a plurality of points in the set range of each preset key point, and determining the average coordinate as the initial coordinate of the preset key point.
The set range may be a range within a set radius of a predetermined key point, may be a circle, or may be selected according to a shape of a human body part, such as a shape of an eye or a shape of a mouth, and is not limited herein.
For example, in acquiring the initial coordinates of the left eye, a plurality of points around the left eye may be obtained, and the average coordinates of the plurality of points are derived as the initial coordinates; the same can be taken to improve the accuracy when acquiring the initial coordinates of the right eye, the nose tip, the left mouth corner and the right mouth corner.
Step S103: and determining a transformation matrix of the first image according to the standard coordinates and the initial coordinates.
In a specific embodiment, the transformation matrix C is determined by the following relation (1),
CX＝Y (1)；
wherein, Y is a matrix formed by standard coordinates, and X is a matrix formed by initial coordinates corresponding to the standard coordinate position.
Specifically, in the process of converting the initial coordinate into the standard coordinate, the initial coordinate needs to be translated by m pixels on the X axis, translated by n pixels in the Y direction, and then rotated clockwise until coinciding with the standard coordinate. In one embodiment, the transformation matrix is assumed to be:
the matrix Y of standard coordinates is:
the matrix X of initial coordinates is:
substituting the formulas (2), (3) and (4) into the formula (1), and knowing the matrix Y formed by the standard coordinates and the matrix X formed by the initial coordinates corresponding to the standard coordinate positions, the values of a, b, m and n in the conversion matrix can be obtained, and further the conversion matrix C can be obtained.
Step S104: and aligning the first images to be aligned based on the conversion matrix to obtain aligned second images.
Specifically, a plurality of initial coordinates X (i.e., all coordinates in the first image) in the first image may be obtained through an image detection algorithm, and are known, and the transformation matrix C is also known, and the initial coordinates X and the transformation matrix C are substituted into formula (1) to obtain a plurality of standard coordinates, and the plurality of standard coordinates constitute the second image. The second image is the image after the first image is aligned.
In this embodiment, a transformation matrix is further calculated by obtaining an initial coordinate of a preset key point in a first image to be aligned and a standard coordinate of a preset key point in a standard image, and the transformation matrix is used to further align the first image. The transformation matrix is transformed according to the coordinates of the same preset key points in the first image and the representation image, so that the transformation matrix can be ensured not to deform in the process of transforming the first image into the aligned second image, and the accuracy of image alignment is improved.
In addition, the average coordinate of the coordinates of a plurality of points in the set range of each preset key point is determined as the initial coordinate of the preset key point, so that the coordinate precision of the preset key point can be improved, the calculated transformation matrix is more accurate, the first image is further ensured not to be deformed in the process of being converted into the aligned second image, and the accuracy of image alignment is improved.
Fig. 3 is a schematic flow chart of a face recognition method according to a first embodiment of the present invention.
The face recognition method of the embodiment comprises the following steps:
s201: and acquiring a face image to be recognized and a reference face image.
Face recognition is needed in some scenes, such as entrance guard, online payment face brushing verification and the like. Therefore, to complete the recognition function, a face image to be recognized is first acquired. For example, the camera is used for taking a picture, which is not limited herein.
The reference face image may be an image input when the user registers an account, such as an identification card image or an image captured on site during registration, which is not limited herein.
S202: and if the face image is not the standard image, aligning the face image to obtain the aligned face image.
After a face image to be recognized is acquired, whether the face image is a standard image or not is judged firstly. Specifically, the coordinates of a preset key point or other points in the face image can be obtained by a face detection method, and the coordinates of the point are compared with the coordinates of the same preset key point or other points in the face standard image, and if the coordinates are the same or in a corresponding proportion, the face image is the standard image. If the coordinates of the point are different from the coordinates of the same preset key point or other positions in the standard human face image and are not proportional to the coordinates, the human face image is not the standard human face image.
The face alignment method in this step is the image alignment method of any of the above embodiments, and is not described herein again.
S203: and judging whether the aligned face image is matched with the reference face image.
And if the face images to be recognized are matched, the recognition of the face images to be recognized is successful.
Specifically, if the aligned face image is matched with the reference face image, payment is completed or access control is opened, and a response action after face recognition is successful can be specifically set according to a specific application scene.
If the face recognition is not successful, in an alternative embodiment, an alarm prompt tone may be issued, which is not limited herein.
Different from the prior art, the face recognition method of the embodiment aligns the face images to obtain the aligned face images and recognizes the aligned face images by obtaining the face images to be recognized and determining that the face images to be recognized are not standard images, so that the recognition effect can be more accurate.
Fig. 4 is a schematic structural diagram of an image alignment apparatus according to a first embodiment of the present invention. Comprises an acquisition module 11, an initial coordinate acquisition module 12, a transformation matrix acquisition module 13 and a transformation module 14. The obtaining module 11 is configured to obtain a first image to be aligned and a standard coordinate of a preset key point in a standard image. The initial coordinate obtaining module 12 is configured to obtain initial coordinates of preset key points from the first image. The transformation matrix obtaining module 13 is configured to determine a transformation matrix according to the standard coordinate and the initial coordinate; the conversion module 14 is configured to align the first image to be aligned based on the transformation matrix, so as to obtain an aligned second image.
Fig. 5 is a schematic structural diagram of a face recognition device according to a first embodiment of the present invention. The method comprises the following steps: an acquisition module 21, an image alignment module 22 and a judgment module 23.
The obtaining module 21 is configured to obtain a face image to be recognized and a reference face image. The image alignment module 22 is configured to align the face image when the face image is not the standard image, so as to obtain an aligned face image. The aligned face image is obtained by the image alignment method described in fig. 1 to 2. The judging module 23 is configured to judge whether the aligned face image matches with the reference face image; and when the face images are matched, the face images to be recognized are determined to be successfully recognized. And when the face images are not matched, determining that the face images to be recognized are failed to recognize.
Fig. 6 is a schematic structural diagram of an intelligent system according to a first embodiment of the present invention. Including a memory 52, a processor 51 and a control circuit 53 connected to each other. The memory 52 is used for storing program instructions for implementing the image alignment method and the face recognition method of any one of the above.
The processor 51 is operative to execute program instructions stored in the memory 52.
The processor 51 may also be referred to as a CPU (Central Processing Unit). The processor 51 may be an integrated circuit chip having signal processing capabilities. The processor 51 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 52 may be a memory bank, a TF card, etc., and may store all information in the image alignment apparatus and the face recognition apparatus, including the input raw data, the computer program, the intermediate operation result, and the final operation result, which are stored in the memory. It stores and retrieves information based on the location specified by the controller. With the memory, the image alignment device and the face recognition device have memory function, so that normal work can be guaranteed. The memory is classified into a main memory (internal memory) and an auxiliary memory (external memory) according to the purpose, and also into an external memory and an internal memory. The external memory is usually a magnetic medium, an optical disk, or the like, and can store information for a long period of time. The memory refers to a storage component on the main board, which is used for storing data and programs currently being executed, but is only used for temporarily storing the programs and the data, and the data is lost when the power is turned off or the power is cut off.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a system server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method of the embodiments of the present application.
FIG. 7 is a schematic structural diagram of a storage medium according to the present invention. The storage medium of the present application stores a program file 61 capable of implementing all the image alignment methods and the face recognition methods, where the program file 61 may be stored in the storage medium in the form of a software product, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the methods according to the embodiments of the present application. The aforementioned storage device includes: various media capable of storing program codes, such as a usb disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or terminal devices, such as a computer, a server, a mobile phone, and a tablet.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An image alignment method, comprising:
acquiring a first image to be aligned and a standard coordinate of a preset key point in a standard image;
acquiring initial coordinates of preset key points from the first image;
determining a transformation matrix of the first image according to the standard coordinate and the initial coordinate;
and aligning the first images to be aligned based on the conversion matrix to obtain aligned second images.
2. The method of claim 1,
the determining the transformation matrix of the first image according to the standard coordinate and the initial coordinate specifically includes:
determining a transformation matrix C for the first image by the following relation (1),
CX-Y (1)；
wherein, Y is a matrix formed by standard coordinates, and X is a matrix formed by the initial coordinates.
3. The method of claim 1, wherein the obtaining initial coordinates of preset keypoints from the first image comprises:
respectively acquiring coordinates of a plurality of points in a set range of each preset key point;
calculating the average coordinate of the coordinates of a plurality of points in the set range of each preset key point, and determining the average coordinate as the initial coordinate of the preset key point.
4. The method of claim 1, wherein the acquiring the first image to be aligned comprises:
acquiring an image to be aligned;
detecting the position of a human face in the image to be aligned by using a human face detection algorithm;
and intercepting a picture with the position of the face as the first image.
5. The method according to any one of claims 1 to 4, wherein the preset key points comprise at least a left eye, a right eye, a nose tip, a left mouth corner and a right mouth corner of a human face.
6. A face recognition method, comprising:
acquiring a face image to be recognized and a reference face image;
if the face image is not a standard image, aligning the face image to obtain an aligned face image, wherein the aligned face image is obtained by the image alignment method according to any one of claims 1 to 5;
judging whether the aligned face image is matched with the reference face image;
and if the face images to be recognized are matched, the recognition of the face images to be recognized is successful.
7. An image alignment device is characterized by comprising an acquisition module, an initial coordinate acquisition module, a transformation matrix acquisition module and a transformation module,
the acquisition module is used for acquiring a first image to be aligned and a standard coordinate of a preset key point in a standard image;
the initial coordinate acquisition module is used for acquiring initial coordinates of preset key points from the first image;
the transformation matrix acquisition module is used for determining a transformation matrix according to the standard coordinate and the initial coordinate;
the conversion module is used for aligning the first image to be aligned based on the conversion matrix to obtain an aligned second image.
8. A face recognition apparatus, comprising: the device comprises an acquisition module, an image alignment module and a judgment module;
the acquisition module is used for acquiring a face image to be recognized and a reference face image;
the image alignment module is used for aligning the face image when the face image is not a standard image to obtain an aligned face image; wherein the aligned face image is obtained by the image alignment method according to any one of claims 1 to 5;
the judging module is used for judging whether the aligned face image is matched with the reference face image; and when matching, determining that the face image to be recognized is successfully recognized.
9. An intelligent system, comprising: a control circuit, a processor and a memory coupled to each other, wherein,
the memory is for storing program instructions for implementing the image alignment method of any one of claims 1-5; or
The memory is used for storing program instructions for implementing the face recognition method according to any one of claim 6;
the processor is configured to execute the program instructions stored by the memory.
10. A storage medium characterized by storing a program file executable to implement the image alignment method according to any one of claims 1 to 5; or
The program file is executable to implement the face recognition method of any one of claim 6.
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| 任梓涵: "基于深度学习的实时视频人脸识别方法的研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI796063B (en) * | 2021-12-23 | 2023-03-11 | 新加坡商瑞昱新加坡有限公司 | Liveness detection method and system threrof |
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