A palm correction method based on non-homologous double purposes

Abstract

A palm correction method based on non-homologous double purposes, comprising the following steps: step S1: detecting the palm of a first image I ir and a second image I rgb respectively, and obtaining the palm region ROI ir of the first image I ir and the palm region ROI rgb of the second image I rgb respectively; wherein the first image and the second image are non-homologous images; step S2: extracting the palm edge of the palm region ROI ir of the first image and the palm region ROI rgb of the second image, and generating new images E ir and E rgb respectively; step S3: registering the palm edge of the palm region ROI ir of the first image I ir and the palm region ROI rgb of the second image I rgb , and calculating the disparity d of the first image I ir and the second image I rgb ; step S4: calculating the depth of the palm edge according to the disparity d; step S5: calculating the palm orientation according to the depth of the palm edge; step S6: correcting the first image and the second image according to the palm orientation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of palm recognition cameras, in particular, to a palm correction method based on non-homologous binoculars. BACKGROUND

[0002] Since the lines of a human palm are more stable, palm recognition is a more stable and secure biometric technology than face recognition, and thus can be used to identify a person's identity through palm recognition, and further used in security checks, payments, identity recognition and other fields. Palm recognition is a technology with broad application prospects.

[0003] Existing non-contact palm recognition devices are divided into two types: one has both palm print and palm vein recognition functions; the other only uses palm vein information for recognition, greatly simplifying the system, and since palm veins belong to coarse-grained features, using a lower resolution can well ensure the palm vein information, but correspondingly limits the accuracy of palm vein recognition. In addition, the imaging quality of palm veins is greatly affected by light sources, palm distance and posture, and unstable images are easily collected. Especially in the prior art, the processing method of image binarization is used for comparison with the original image, which improves the data quality to some extent, but still cannot meet the needs of commercial applications. SUMMARY

[0004] Therefore, the present application uses a non-homologous binocular system composed of a first camera and a second camera to directly process the first image and the second image, improves the quality of image acquisition, and corrects the palm orientation to process a larger angle of the palm, while the accuracy of correction is greatly improved.

[0005] In a first aspect, the present application provides a palm correction method based on non-homologous binoculars, characterized in that it comprises the following steps:

[0006] Step S1: detecting a palm in a first image I ir and a second image I rgb respectively, and obtaining a palm region ROI ir of the first image I ir and a palm region ROI rgb of the second image I rgb respectively; wherein the first image and the second image are non-homologous images;

[0007] Step S2: extracting the palm edge of the palm region ROI ir of the first image and the palm region ROI rgb of the second image, and generating new images E ir and E rgb respectively;

[0008] Step S3: For the first image I ir palm area ROI ir and the second image I rgb Palm area POI rgb The edges of the palm are registered, and the first image I is calculated. ir and the second image I rgb The parallax d;

[0009] Step S4: Calculate the depth of the palm edge based on the parallax d;

[0010] Step S5: Calculate the palm orientation based on the depth of the palm edge;

[0011] Step S6: Based on the palm orientation, compare the first image I ir and the second image I rgb Perform corrections.

[0012] Optionally, the aforementioned hand correction method based on non-homogeneous binoculars is characterized in that, before step S1, it further includes:

[0013] Step S0: Correct the distortion of the original first image and the original second image respectively, and then perform epipolar correction to obtain the corrected first image I. ir and the corrected second image I rgb .

[0014] Optionally, the hand correction method based on non-homogeneous binoculars is characterized in that, in step S1, the first image I is further processed. ir and the second image I rgb Based on the palm region ROI ir and the palm region ROI rgb Perform image segmentation and set the non-palm regions to zero.

[0015] Optionally, the hand correction method based on non-homogeneous binoculars is characterized in that step S2 includes:

[0016] Step S21: Process the first image I respectively ir and the second image I rgb According to the first image I ir palm area ROI ir and the second image I rgb palm area ROI rgb Perform image segmentation and set the non-palm regions to zero;

[0017] Step S22: On the image obtained in step S21, apply the first image I... ir palm area ROIir and the second image I rgb palm region ROI rgb palm edge, and generate new images E ir and E rgb .

[0018] Optionally, the palm correction method based on non-homologous double purposes has the feature that the step S3 comprises:

[0019] Step S31: registering the palm region ROI ir of the first image I ir and the palm region ROI rgb of the second image I rgb palm edge.

[0020] Step S32: checking the registration and eliminating the wrong matching.

[0021] Step S33: calculating the disparity d of the first image I ir and the second image I rgb .

[0022] Optionally, the palm correction method based on non-homologous double purposes has the feature that the step S5 comprises:

[0023] Step S51: calculating the first three-dimensional space position according to the depth of the palm edge.

[0024] Step S52: fitting the first three-dimensional space position in space to obtain the first plane, and judging the bending degree of the palm according to the residual distribution and size.

[0025] Step S53: if the bending degree of the palm is normal, only fitting the edge of the palm region in space to obtain the second plane, and taking the normal vector of the second plane as the palm orientation.

[0026] Optionally, the palm correction method based on non-homologous double purposes has the feature that the step S6 comprises:

[0027] Step S61: dividing the image into the first stretching area, the first compression area and the first repair area according to the palm orientation, the gray value and the distance from the edge on the second image, and copying the areas to the first image; wherein the first image is the texture image of the palm, and the second image is the vein image of the palm.

[0028] Step S62: on the first image, according to the texture features, fine-tuning the first stretching area, the first compression area and the first repair area to obtain a second stretching area, a second compression area and a second repair area respectively, and copying the second stretching area, the second compression area and the second repair area to the second image;

[0029] Step S63: on the first image and the second image, stretching the second stretching area by the same amplitude, compressing the second compression area by the same amplitude, and repairing the second repair area respectively.

[0030] Optionally, the palm correction method based on non-homologous double purposes has the characteristics that before the processing of the image in any step, the first image I ir , the second image I rgb , the new image E ir or the new image E rgb are compressed.

[0031] In the second aspect, the application provides a palm correction device based on non-homologous double purposes, which has the characteristics that it comprises:

[0032] a processor;

[0033] a memory module, wherein executable instructions of the processor are stored in the memory module;

[0034] The processor is configured to execute the steps of the palm correction method based on non-homologous double purposes by executing the executable instructions.

[0035] In the third aspect, the application provides a computer readable storage medium for storing a program, wherein the program is executed to realize the steps of the palm correction method based on non-homologous double purposes.

[0036] Compared with the prior art, the application has the following advantages:

[0037] The application uses the first image and the second image as original data, without the need for p-sensor and other devices, so that the input conditions for palm recognition are reduced, thereby the corresponding hardware device can be simplified, the volume is smaller, and the device is easy to integrate, which is beneficial to the miniaturization of the device.

[0038] The image adopted by the present application can be shared with other palm recognition functions, so that one image can be used for multiple functions, thereby maximizing the function of one image and saving steps and device space. For example, when the first image is a color image, it can be used not only for correction and reconstruction, but also for palmprint recognition; when the second image is an infrared image, it can be used not only for correction and reconstruction, but also for living body detection, and the present application corrects two different types of images, so that the subsequent image processing has more dimensional data, thereby improving the processing quality.

[0039] In the prior art, palmprint recognition and palm vein features are processed by the same image, but the processing of palmprint and vein is not good, and the present application uses non-homologous cameras to recognize palmprint and palm vein respectively, so that the image quality is high and the effect is good.

[0040] According to the present application, the palm orientation is calculated by the palm edge depth, and then the palm image is corrected according to the palm orientation, so that the calculation process is simple and efficient, the data is reliable and accurate, the response speed of the present application is faster than other schemes, and the fast response requirement of commercial application can be met. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on the provided drawings. Other features, objects and advantages of the present application will become more apparent through reading the following detailed description of the non-limiting embodiments with reference to the accompanying drawings:

[0042] Figure 1 A step flow chart of a palm correction method based on non-homologous double objectives in an embodiment of the present application;

[0043] Figure 2 A palm detection image in an embodiment of the present application;

[0044] Figure 3 A palm edge image in an embodiment of the present application;

[0045] Figure 4 A palm edge registration image in an embodiment of the present application;

[0046] Figure 5 A palm edge depth image in an embodiment of the present application;

[0047] Figure 6 A step flow chart of acquiring a new image in an embodiment of the present application;

[0048] Figure 7 A flow chart of a step of calculating the disparity of the first image and the second image in an embodiment of the present application;

[0049] Figure 8 A palm edge disparity map in an embodiment of the present application;

[0050] Figure 9 A flow chart of a step of calculating the palm orientation according to the palm edge depth in an embodiment of the present application;

[0051] Figure 10 A flow chart of a step of image correction in an embodiment of the present application.

[0052] Figure 11 A structural schematic diagram of a palm correction device based on non-homologous double purposes in an embodiment of the present application;

[0053] Figure 12 A computer readable storage medium in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The present application will be described in detail below with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made. These are within the scope of the present application.

[0055] The terms "first", "second", "third", "fourth" and the like in the description, claims, and drawings of the present application, and the above-mentioned drawings (if any) are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein, for example, can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0056] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in some embodiments.

[0057] The palm correction method based on non-homologous double purposes provided by the embodiments of the present application aims to solve the problems in the prior art.

[0058] The technical solutions of the present invention and how they solve the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0059] Figure 1 This is a flowchart illustrating the steps of a hand correction method based on non-homogeneous binocular vision according to an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a hand correction method based on non-homogeneous binocular vision, which includes the following steps:

[0060] Step S1: For the first image I ir Second image I rgb The palms are detected separately, and the first image I is obtained separately. ir palm area ROI ir and the second image I rgb palm area ROI rgb .

[0061] In this step, the first image and the second image are non-homogeneous images. A handprint detection model is used for the first image I. ir and the second image I rgb Each image is detected separately to determine if a hand is present. If a hand is present, the first image I is obtained. ir palm area ROI ir and the second image I rgb palm area ROI rgb .like Figure 2 As shown, the ROI of the palm region ir and palm area ROI rgb All rectangles are the smallest rectangles that include the palm, meaning that all four sides of the rectangle are tangent to the edge of the palm. The palm detection model used in this step can be any model that can perform palm detection; this embodiment does not impose any restrictions on it.

[0062] In some embodiments, the first image I is respectively... ir and the second image I rgb Based on the palm region ROI ir and the palm region ROI rgb Image segmentation is performed, and non-palm regions are zeroed out. By zeroing out non-palm regions, the contrast between the palm region and other regions is enhanced, resulting in better subsequent computation and processing with less processing power.

[0063] In some embodiments, this method is adapted for continuous shooting. The hand region in each frame is obtained by subtracting the previous frame from the subsequent frame. Since the hand is not fixed in place by a palm rest, it is difficult to keep the hand in the same position. Therefore, subtracting two frames taken at different times can quickly locate the edge of the hand, thereby obtaining the hand contour information and locating the hand region. At the same time, the image subtraction method is simple and fast.

[0064] In some embodiments, before performing the image processing in this step, the first image I is... ir Second image I rgb Compress the first image I. ir Second image I rgb The compression ratios differ. For example, before performing this step, the first image I... ir and the second image I rgb Compression is performed separately at ratios of 4 and 2, respectively, to make the image sizes more similar and ensure processing accuracy. Preferably, the closer the palm is to the camera, the higher the compression ratio; the farther the palm is from the camera, the lower the compression ratio. When the palm occupies 70% of the image area, the compression ratio of the first image is no less than 5; when the palm occupies 50% of the image area, the compression ratio of the first image is no less than 2. The compression ratio of the first image is more than three times that of the second image.

[0065] Step S2: Analyze the palm region ROI of the first image. ir and the palm region ROI of the second image rgb The edges of the palm are extracted, and new images E are generated. ir and E rgb .

[0066] In this step, the edges of the palm are extracted using an edge extraction algorithm, and new images E are generated. ir and E rgb Edge extraction algorithms can be implemented using various methods, such as those based on designed edge extraction operators (convolutional templates). These edge extraction operators include, but are not limited to, Sobel, Prewitt, Robert, and LoG. Edge extraction algorithms can also be obtained using adaptive algorithms or machine learning-trained models; this embodiment does not impose any limitations on these methods. Figure 3 As shown, the identified palm edge has a certain width, and because the grasping object is the same palm, the edge also has very good consistency compared to the inside of the palm, which can achieve better processing results.

[0067] Step S3: For the first image I ir palm area ROIir and the second image I rgb hand palm region ROI rgb are registered by the hand palm edge, and the parallax d of the first image I ir and the second image I rgb is calculated.

[0068] In this step, the new images E ir and E rgb are registered by the hand palm edge, and the parallax d of the first image I ir and the second image I rgb is calculated. As shown in Figure 4 , when registering the hand palm edge, all edge points can be matched, or only part of the points can be matched. Figure 4 In the embodiment, 68 labeled points are used for registration, which can achieve a balance between effective representation of the hand palm and calculation amount. Preferably, the number of labeled points used when registering the hand palm edge is 39-136. If the number is less than 39, the shape of the hand palm cannot be fully represented; and if the number is greater than 136, the calculation amount is too large, which consumes too many computing resources, but the recognition effect is improved very limitedly.

[0069] In some embodiments, before performing the processing of the images in this step, the new images E ir and E rgb are compressed. The compression of the new images E ir and E rgb is the same as the compression of the first image I ir and the second image I rgb in the foregoing step, which will not be described here again.

[0070] Step S4: calculating the depth of the hand palm edge according to the parallax d.

[0071] In this step, after the parallax d of the first image I ir and the second image I rgb is calculated, the depth of the hand palm edge and the depth of the hand palm center can be calculated by using the binocular principle. The hand palm center is the point with the smallest difference between the distance to the left and right sides and the distance to each hand palm root. As shown in Figure 5 , the hand palm edge is the edge in the three-dimensional space, which still appears as a line with a certain width when projected on the two-dimensional plane. On the hand palm edge depth map, only the depth data of the hand palm edge is present, and the data of other parts is 0.

[0072] Step S5: calculating the hand palm orientation according to the hand palm edge depth.

[0073] In this step, according to the palm edge depth, the three-dimensional space information of the palm edge can be obtained, so that the palm orientation can be calculated. In the calculation of the palm orientation, different schemes can be adopted.

[0074] In some embodiments, a plurality of palm region identification points are taken to calculate the plane where the identification points are located, and the normal vector of the plane is taken as the palm orientation. The palm region is the palm part except the fingers. The plane where the identification points are located is obtained by using the positional relationship of the identification points in the three-dimensional space. It should be noted that the three-dimensional space is the three-dimensional space relative to the non-homologous binocular camera, rather than the three-dimensional space in the world coordinate system.

[0075] In some embodiments, the palm center position is calculated according to the palm edge position, the palm center depth is calculated according to the palm edge depth, and the normal vector of the palm center is taken as the palm orientation. In the calculation of the palm center depth, the mean value of the palm edge depth is taken. Preferably, different weight values are given according to the distance between the palm edge and the palm center. The closer the distance, the higher the weight. When the position and depth value of the palm center are obtained, the normal vector of the pixel can be calculated, that is, the normal vector of the palm center, so that the palm orientation is obtained.

[0076] Step S6: correcting the first image and the second image according to the palm orientation.

[0077] In this step, according to the palm orientation, the palm image is corrected to the direction perpendicular to the camera lens. Since the pre-stored palm information is perpendicular to the lens shooting, the palm needs to be corrected to the direction perpendicular to the lens. In some embodiments, the palm orientation is provided for the pre-stored palm information, and then the current palm image is corrected to the same palm orientation as the pre-stored palm information to improve consistency.

[0078] In some embodiments, the step S1 further includes:

[0079] Step S0: respectively distorting and correcting the original first image and the original second image, and then performing polar line correction to obtain the corrected first image I ir and the corrected second image I rgb .

[0080] In this step, the original first image and the original second image are non-homologous images, that is, images obtained by using different technologies. The original first image is distorted and corrected, and then polar line correction is performed to obtain the corrected first image I ir . The original second image is respectively distorted and corrected, and then polar line correction is performed to obtain the corrected second image i rgbThe distortion is caused by the lens imaging principle, so the distortion correction of the original first image and the original second image needs to be corrected according to the respective parameters of the acquisition device. The epipolar rectification is a correction for the binocular system, which is to rotate the two cameras and redefine a new image plane, so that the epipolar lines are collinear and parallel to a certain coordinate axis (usually the horizontal axis) of the image plane, which simultaneously establishes a new stereo image pair. After the correction, the same matching point pair is located in the same row of the two views, which means that they only have the difference of the horizontal coordinates (or column coordinates), and this difference is called the parallax. However, since the images used are the first image and the second image, there are differences in the content they capture, and the parallax cannot be directly solved by the current technology. When the first camera captures the palm image, the veins have a certain absorption effect on infrared light, causing the veins to be relatively dark; while the second camera captures the palm image, it mainly images the surface texture of the palm, and the imaging of the two palm textures is difficult to match directly. At the same time, the palms of people with different body shapes and physical conditions differ greatly, making the difference between the infrared image and the second image even greater, making it difficult to effectively match. This step corrects the two images to make the data more accurate, thereby making the subsequent matching more accurate. It should be noted that the original first image and the original second image used in this embodiment are usually obtained by a calibrated binocular system, and one of the binocular systems is a first camera and the other is a second camera. Among them, the first camera is used to acquire the first image, the second camera is used to acquire the second image, and the first camera and the second camera acquire the target image at the same time. For example, the first camera is a near-infrared camera, the first image is a near-infrared image, the second camera is a color camera, and the second image is a color image.

[0081] This step makes the image with large distortion also better processed, so that the palm distance from the shooting device is close, that is, the FOV is large, and accurate results can also be obtained, thereby improving the effective recognition distance range of the palm.

[0082] Figure 6 This is a flowchart of a step of acquiring a new image in an embodiment of the present application. As shown in Figure 6 , unlike the previous embodiments, the method for acquiring a new image in an embodiment of the present application includes the following steps:

[0083] Step S21: respectively performing image segmentation on the palm region ROI ir of the first image I rgb and the palm region ROI rgb of the second image I rgb according to the palm region ROI ir of the first image I ir and the palm region ROI rgb of the second image I rgb , and setting the non-palm region to zero.

[0084] In this step, in addition to segmenting the image, the non-palm region is also set to zero to improve the extraction effect of the subsequent edge extraction algorithm and to cope with more complex scenes. When the palm wears rings, hand ornaments and other types of decorations, the scene will be more complex, so setting the non-palm region to zero can make the subsequent processing easier.

[0085] Step S22: On the image obtained in step S21, the palm edge of the first image I ir The palm region ROI ir and the palm edge of the second image I rgb The palm region ROI rgb are extracted, and new images E ir and E rgb are generated respectively.

[0086] In this step, only the palm region needs to be processed and the palm edge is extracted, so that the processing area is smaller and the processing speed is faster. The new images E ir and E rgb only contain the palm edge and can be used for processing in subsequent steps, thereby greatly reducing the processing amount and improving the efficiency.

[0087] Figure 7 is a flow chart of a step of calculating the disparity of the first image and the second image in an embodiment of the present application. As shown in Figure 7 Unlike the foregoing embodiments, the method for calculating the disparity of the first image and the second image in an embodiment of the present application comprises the following steps:

[0088] Step S31: The palm edge of the palm region ROI ir of the first image I ir and the palm region ROI rgb of the second image I rgb are registered.

[0089] In this step, the palm edges in the two images are first registered. According to prior knowledge, the information of ROI ir and ROI rgb , the edges are registered using a traditional method or deep learning. Preferably, during registration, a feature-based method is used, and the labeled points in the foregoing embodiments are feature points. Through registration, the registration of the two images can be realized.

[0090] Step S32: The registration is checked and incorrect matches are removed.

[0091] In this step, according to prior knowledge, the wrong matching is removed; such as disparity needs to have consistency, topological relationship, etc. Compared with the matching scheme in the prior art, the two images matched in the embodiment are obtained by different technologies, so there will be wrong matching due to different technical characteristics, but these usually show a certain regularity. When removing the wrong matching, a corresponding filtering model can also be used according to different technical characteristics.

[0092] Step S33: The disparity d of the first image I ir and the second image I rgb is calculated.

[0093] In this step, the palm edge of the palm region ROI ir of the first image I ir and the palm edge of the palm region POI rgb of the second image I rgb is calculated, that is, the disparity of the first image I ir and the second image I rgb is obtained. As shown in the figure, the disparity d is obtained by calculating x1 and x2. Figure 8

[0094] The embodiment calculates the disparity of the two images by registering the palm edge of the palm region and removing the wrong matching, so as to improve the quality of registration, make the calculation of disparity more accurate, and make the subsequent data more accurate.

[0095] Figure 9 It is a step flow chart for calculating the palm orientation according to the palm edge depth in the embodiment of the application. As shown in the figure, unlike the previous embodiment, the method for calculating the palm orientation according to the palm edge depth in the embodiment of the application comprises the following steps: Figure 9

[0096] Step S51: Calculate the first three-dimensional space position according to the depth of the palm edge.

[0097] In this step, after obtaining the depth data of the palm edge by using the non-homologous binocular system according to the principle of disparity, the three-dimensional space position can be further calculated. The three-dimensional space coordinates can be selected relative to the coordinate system of the binocular system, or the world coordinate system. If the coordinate system relative to the binocular system is selected, the palm orientation finally obtained by the embodiment is the orientation relative to the binocular system. If the world coordinate system is selected, the palm orientation finally obtained by the embodiment is the orientation in the world coordinate system. Which coordinate system to select can be selected according to application requirements in the embodiment.

[0098] ​​Step S52: performing spatial plane fitting on the first three-dimensional spatial position to obtain a first plane, and judging the bending degree of the palm according to the residual distribution and size.

[0099] In this step, the bending degree of the palm is evaluated by using the first plane. The first three-dimensional spatial position is the three-dimensional spatial position of all the palm edges. The first plane obtained by performing spatial plane fitting on the first three-dimensional spatial position is the most concentrated plane. When the palm is fully unfolded, the first plane is closest to the first three-dimensional spatial position, and the fitting effect is the best. When the palm is not fully unfolded, the bending degree can be judged according to the residual distribution and size. When the palm is fully unfolded, the residual is not greater than half of the thickness of the palm. Considering the state difference of the palm of different persons when naturally unfolded, the threshold value for evaluating the residual can be slightly larger than the general palm thickness. Since the fingers have multiple joints, they can change in multiple degrees, so when judging the bending degree of the palm, the residual distribution and the residual size need to be combined for analysis. When the residual distribution is in a narrow rectangular region, if the side of the rectangle is less than the threshold value, it is judged that the palm degree is normal. When the residual distribution presents a parabolic shape or a normal distribution, it is judged that the palm is naturally curved, and at this time, the bending degree is judged whether it is sufficient for palm recognition to give a normal or abnormal evaluation. When the residual distribution presents a relatively dense irregular shape, it is judged that the palm is in a fist or other invalid state, and the image is discarded.

[0100] Step S53: if the bending degree of the palm is normal, performing spatial plane fitting only on the edges of the palm region to obtain a second plane, and taking the normal vector of the second plane as the palm orientation.

[0101] In this step, the palm region refers to the part of the palm other than the fingers. If the bending degree of the palm is normal, the palm region is complete, and better spatial plane fitting can be performed. Compared with the first plane, the second plane indicates the spatial position of the palm region, excluding the interference of finger bending. The normal vector of the second plane is the palm orientation.

[0102] The present embodiment saves the processing amount and improves the efficiency by reconstructing the three-dimensional space of the palm edges and processing only the information of the palm edges. The present embodiment evaluates the bending degree of the palm by the first plane, and then eliminates the influence of finger bending by the second plane to obtain an accurate palm orientation.

[0103] Figure 10 A step flowchart of an image correction method is provided in the embodiment of the present application. As shown in Figure 10 Compared with the foregoing embodiment, the image correction method provided in the embodiment of the present application comprises the following steps:

[0104] Step S61: dividing the second image into a first stretching area, a first compression area and a first repair area according to the palm orientation, gray value and distance from the edge, and copying the areas to the first image.

[0105] In this step, the first image is a texture image of the palm, and the second image is a vein image of the palm. The first stretching area, the first compression area and the first repair area are divided according to the subsequent processing method of the image. After the current orientation correction, the image area that becomes larger is the first stretching area, the image area that becomes smaller is the first compression area, and the image area that is blocked due to the angle and needs to be supplemented is the first repair area. The division of the three areas is also applicable to the first image.

[0106] Step S62: on the first image, according to the texture features, fine-tuning the first stretching area, the first compression area and the first repair area to obtain a second stretching area, a second compression area and a second repair area respectively, and copying the second stretching area, the second compression area and the second repair area to the second image.

[0107] In this step, the texture features on the palm are important information for comparing the palm with the pre-stored palm information, and therefore the texture features are important factors for correcting the palm. Important lines on the palm include life line, success line, emotion line, wisdom line, marriage line, etc., and these lines need to be kept clear and coherent during correction, so the division of each area needs to be fine-tuned to make the division more reasonable.

[0108] Step S63: on the first image and the second image, stretching the second stretching area by the same amplitude, compressing the second compression area by the same amplitude, and repairing the second repair area respectively.

[0109] In this step, the stretching or compression amplitude of the first image and the second image is the same at the same position. The stretching amplitude of the second stretching area at different positions can be different, and the compression amplitude of the second compression area at different positions can be different. For the second repair area, separate repair is performed for the first image and the second image. For the second repair area, due to the different positions or the degree of finger bending, part of the content is blocked, so repair needs to be performed on the first image and the second image. When repairing, the palm print or vein information can be used for repair.

[0110] The present embodiment subdivides the palm into different areas, making the operation of correcting the palm more precise and better, and considering the blocked part, it can handle a larger angle, expand the palm range supported by the palm recognition, and make the palm application more convenient and fast.

[0111] The embodiment of the present application also provides a palm correction device based on non-homologous binoculars, comprising a processor and a memory having executable instructions of the processor stored therein. The processor is configured to perform the steps of the palm correction method based on non-homologous binoculars by executing the executable instructions.

[0112] As described above, the first image and the second image are acquired by using the depth camera of the binocular system composed of the first camera and the second camera, and the two different types of images are corrected by the method in the foregoing embodiment, so that the differences between the different types of images are overcome, and the purpose of accurate correction is achieved.

[0113] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "platform" here.

[0114] Figure 11 is a structural schematic diagram of a palm correction device based on non-homologous binoculars in the embodiment of the present application. The electronic device 600 according to this embodiment of the present application will be described below with reference to Figure 11 Figure 11 The electronic device 600 shown is merely an example, and should not impose any limitation on the functions and use range of the embodiment of the present application.

[0115] As shown in Figure 11 , the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 can include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0116] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present application described in the above-mentioned palm correction method based on non-homologous binoculars part of the specification. For example, the processing unit 610 can perform the steps as shown in Figure 1

[0117] The storage unit 620 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache memory unit 6202, and can further include a read-only memory (ROM) 6203.

[0118] ​​The storage unit 620 can also include a number of program modules 6205 that are stored in the memory 6204, that include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, and each of these examples, or some combination thereof, can include implementation of a network environment.

[0119] The bus 630 can represent one or more of several types of bus structures, including a storage unit bus or bus controller, a peripheral bus, a graphics acceleration port, a processing unit bus, or a local bus using any of a variety of bus architectures.

[0120] The electronic device 600 can also communicate with one or more external devices 700 such as a keyboard or pointing device, a Bluetooth device, etc.; other devices that enable a user to interact with the electronic device 600; and / or one or more devices that enable the electronic device 600 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 650. Still yet, the electronic device 600 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, through a network adapter 660. The network adapter 660 can be any of a variety of modems, including cable modems, telephone modem, and fiber optic modems. The network adapter 660 can be communicatively coupled to the other components of the electronic device 600 via the bus 630. It should be appreciated that the bus 630 can be one of a variety of bus architectures, including, for example, a Video Electronics Standards Association (VESA) local bus, an Industry Standard Architecture (ISA) bus, an Enhanced ISA bus, an Advanced Graphics Port (AGP) bus, and / or a Peripheral Component Interconnect (PCI) bus. Figure 11 Other hardware and / or software modules that can be used in conjunction with the electronic device 600, such as microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. are not shown in FIG. 6, but can be incorporated into the electronic device 600.

[0121] The embodiment of the present application also provides a computer readable storage medium for storing a program, the program being executed to implement the steps of the palm correction method based on non-homologous double purposes. In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program codes for causing the terminal device to perform the steps of various exemplary embodiments of the present application described in the above-mentioned palm correction method based on non-homologous double purposes part of the specification when the program product is run on the terminal device.

[0122] As shown above, the program of the computer readable storage medium of the embodiment, when executed, acquires the first image and the second image by using the depth camera of the binocular system composed of the first camera and the second camera, and corrects the two different types of images by the method in the foregoing embodiment, overcomes the difference between the different types of images, and achieves the purpose of stable and fast correction.

[0123] Figure 12is a structural schematic diagram of a computer readable storage medium in an embodiment of the present application. Referring to Figure 12 As shown, a program product 800 for implementing the above method according to the embodiment of the present application is described, which can adopt a portable compact disc read-only memory (CD-ROM) and include program codes, and can run on a terminal device, such as a personal computer. However, the program product of the present application is not limited to this, and in this document, the readable storage medium can be any tangible medium containing or storing programs, which can be used or combined with an instruction execution system, device or apparatus.

[0124] The program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0125] The computer readable storage medium can include a data signal carried in the baseband or as a part of a carrier wave propagating through the transmission medium, in which readable program codes are borne. Such a propagating data signal can adopt various forms, including but not limited to electromagnetic signals, optical signals or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit programs for use by or in combination with an instruction execution system, device or apparatus. The program codes contained on the readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0126] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0127] In this embodiment of the invention, a first image and a second image are acquired by a depth camera of a binocular system consisting of a first camera and a second camera. The two different types of images are corrected by the method described in the foregoing embodiment, overcoming the differences between the different types of images and achieving the purpose of stable and fast correction.

[0128] The various embodiments described in this specification are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0129] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A palm correction method based on non-homologous double purposes, characterized in that, The method comprises the following steps: Step S1: detecting a palm in a first image I ir and a second image I rgb respectively, to obtain a palm region ROI ir of the first image I ir and a palm region ROI rgb of the second image I rgb respectively; wherein the first image and the second image are non-homologous images; Step S2: extracting the palm edge of the palm region ROI of the first image ir and the palm region ROI of the second image rgb , and generating new images E ir and E rgb , respectively; Step S3: For the first image I ir palm area ROI ir and the second image I rgb palm area ROI rgb The edges of the palm are registered, and the first image I is calculated. ir and the second image I rgb The parallax d; Step S4: calculating the depth of the palm edge according to the parallax d; Step S5: calculating the palm orientation according to the depth of the palm edge; Step S6: correcting said first image I ir and said second image I rgb according to said palm orientation. The step S6 comprises: Step S61: dividing the second image into a first stretching area, a first compression area and a first repair area according to the palm orientation, the gray value and the distance from the edge, and copying the areas to the first image; wherein the first image is a texture image of the palm, and the second image is a vein image of the palm; Step S62: adjusting the first stretching area, the first compression area and the first repair area according to the texture features to obtain a second stretching area, a second compression area and a second repair area, respectively, and copying the second stretching area, the second compression area and the second repair area to the second image; Step S63: stretching the second stretching area by the same amplitude, compressing the second compression area by the same amplitude, and repairing the second repair area respectively on the first image and the second image.

2. The palm correction method based on non-homologous double purposes according to claim 1, characterized in that, Before the step S1, the method further comprises: Step S0: Distortion correction is performed on the original first image and the original second image respectively, and then polar correction is performed to obtain a corrected first image I ir and a corrected second image I rgb .

3. The palm correction method based on non-homologous double purposes according to claim 1, characterized in that, In step S1, the first image I ir and the second image I rgb are also segmented with respect to the palm region ROI ir and the palm region ROI rgb respectively, and non-palm regions are set to zero.

4. The palm correction method based on non-homologous double purposes according to claim 1, characterized in that, The step S2 comprises: Step S21: respectively performing image segmentation on the first image I ir and the second image I rgb according to the palm region ROI ir of the first image I ir and the palm region ROI rgb of the second image I rgb and setting non-palm regions to zero; Step S22: on the image obtained in step S21, the palm region ROI of the first image I ir is extracted and a new image E ir is generated. rgb Step S23: on the image obtained in step S22, the palm region ROI of the second image I rgb is extracted and a new image E ir is generated. rgb Step S24: the image E ir is compared with the image E rgb .

5. The palm correction method based on non-homologous double purposes according to claim 1, characterized in that, The step S3 comprises: Step S31: registering the palm region ROI of the first image I ir and the palm region ROI of the second image I ir rgb rgb ​​​ Step S32: checking the registration and eliminating the wrong matching; Step S33: calculating the disparity d of the first image I ir and the second image I rgb .

6. The palm correction method based on non-homologous double purposes according to claim 1, characterized in that, The step S5 comprises: Step S51: calculating a first three-dimensional space position according to the depth of the palm edge; Step S52: performing spatial plane fitting on the first three-dimensional space position to obtain a first plane, and judging the bending degree of the palm according to the residual distribution and size; Step S53: if the bending degree of the palm is normal, only performing spatial plane fitting on the edge of the palm area to obtain a second plane, and taking the normal vector of the second plane as the palm orientation.

7. The palm correction method based on non-homologous double purposes according to claim 1, characterized in that, Before the processing of the images in the execution of any of the steps, the first image I ir , the second image I rgb , the new image E ir or the new image E rgb is compressed.

8. A palm correction device based on non-homologous double purposes, characterized by, The method comprises: a processor; a memory module, wherein executable instructions of the processor are stored; wherein the processor is configured to execute the steps of the non-homologous double purpose-based palm correction method of any one of claims 1 to 7 by executing the executable instructions.

9. A computer readable storage medium for storing a program, characterized in that, The program is executed to realize the steps of the non-homologous double purpose-based palm correction method of any one of claims 1 to 7.

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