CN114913574A - Image detection method, living body detection method, electronic device, medium and product - Google Patents

Abstract

The present disclosure relates to an image detection method, a living body detection method, an electronic device, a medium, and a product. The in vivo detection method comprises the following steps: acquiring an image to be detected; wherein the image to be detected comprises a face area of a target object; performing frequency domain conversion on the image to be detected to obtain a frequency spectrum image of the image to be detected, generating a first line image corresponding to the image to be detected based on the frequency spectrum image, and performing copying attack detection on the image to be detected based on the first line image to obtain a copying attack detection result; performing living body detection on the target object based on the image to be detected to obtain a living body detection result; and determining whether the target object is a living body or not based on the living body detection result and the copying attack detection result. The living body detection precision can be improved through the living body detection method and the living body detection device.

Description

Image detection method, living body detection method, electronic device, medium and product

technical field

The present disclosure relates to the technical field of image processing, and in particular, to an image detection method, a living body detection method, an electronic device, a medium and a product.

Background technique

With the development of computer technology, image detection technology has emerged. As an efficient and convenient verification method, it is widely used in many fields. In the related art, for example, in application scenarios such as living body detection and environmental analysis, it is usually necessary to determine whether the detected image is a retaken image. Wherein, the re-photographed image refers to an image obtained by re-photographing the collected image. For example, an image obtained by retaking a picture displayed on the display screen by a mobile phone.

In the related art, it is possible to effectively detect the copied image obtained by copying the display screen by recognizing the moiré pattern. However, usually, only a display with a lower resolution will make the remake image appear more obvious moiré. Therefore, in the related art, it is not possible to effectively detect the reproduced image obtained by reproducing the high-resolution display screen (the reproduced image has no moiré pattern, or the reproduced image has no obvious moiré pattern).

SUMMARY OF THE INVENTION

In order to overcome the problems existing in the related art, the present disclosure provides an image detection method, a living body detection method, an electronic device, a medium and a product.

According to a first aspect of the embodiments of the present disclosure, there is provided a method for detecting a living body, including:

Obtain an image to be detected; wherein, the image to be detected includes the face area of the target object; perform frequency domain conversion on the image to be detected to obtain a spectral image of the image to be detected, and generate a corresponding image based on the spectral image. For the first clue image of the to-be-detected image, performing a copy attack detection on the to-be-detected image based on the first clue image, to obtain a copy attack detection result; and performing a living test on the target object based on the to-be-detected image Detecting to obtain a living body detection result; and determining whether the target object is a living body based on the living body detection result and the remake attack detection result.

In one embodiment, the generating a first clue image corresponding to the image to be detected based on the spectral image includes: intercepting an image area including target pixels according to target rules; wherein the target pixels For the pixel points located in each target pixel row and each target pixel column in the spectrum image, each of the target pixel rows is arranged at equal intervals, and each of the target pixel columns is arranged at equal intervals; splicing each of the image areas , to obtain the first clue image.

In one embodiment, the target pixel row is a pixel row other than the central pixel row of the spectral image, and the target pixel row is a pixel row other than the central pixel row of the spectral image.

In an embodiment, the target pixel row is located at a position corresponding to 1/8 or an integer multiple of 1/8 of the spectrum image, and the target pixel column is located at 1/8 or 1/8 of the spectrum image. at the position corresponding to an integer multiple of .

In one embodiment, the method further includes: performing a brightness difference increasing process on each of the image areas, wherein the brightness difference is a brightness difference between a bright pixel and the remaining pixels in the image area.

In one embodiment, the performing the brightness difference increasing process on each of the image regions includes: for each of the image regions, selecting a specified number of first specified pixel points in descending order of pixel values, Perform adjustment processing on the pixel values of the pixel points other than the first designated pixel point in the image area; wherein, the adjustment processing includes setting the pixel value of the pixel point to a target value or reducing the The pixel value of the pixel point; or, for each of the image areas, determine a second designated pixel point whose pixel value is less than the target pixel threshold, and perform adjustment processing on the pixel value of the second designated pixel point; wherein, the adjustment The processing includes setting the pixel value of the pixel point to a target value or reducing the pixel value of the pixel point.

According to a second aspect of the embodiments of the present disclosure, an image detection method is provided, including:

Obtaining an image to be detected; performing frequency domain conversion on the image to be detected to obtain a spectral image of the image to be detected, and generating a first clue image corresponding to the image to be detected based on the spectral image; The clue image performs copy attack detection on the to-be-detected image to obtain a copy attack detection result.

In one embodiment, the generating a first clue image corresponding to the image to be detected based on the spectral image includes: intercepting an image area including target pixels according to target rules; wherein the target pixels For the pixel points located in each target pixel row and each target pixel column in the spectrum image, each of the target pixel rows is arranged at equal intervals, and each of the target pixel columns is arranged at equal intervals; splicing each of the image areas , to obtain the first clue image.

In one embodiment, the target pixel row is a pixel row other than the central pixel row of the spectral image, and the target pixel row is a pixel row other than the central pixel row of the spectral image.

In an embodiment, the target pixel row is located at a position corresponding to 1/8 or an integer multiple of 1/8 of the spectrum image, and the target pixel column is located at 1/8 or 1/8 of the spectrum image. at the position corresponding to an integer multiple of .

According to a third aspect of the embodiments of the present disclosure, there is provided a living body detection device, comprising:

an acquisition unit, configured to acquire an image to be detected; wherein, the image to be detected includes the face area of the target object; a processing unit, configured to perform frequency domain conversion on the image to be detected, to obtain a spectrum of the image to be detected image, generating a first clue image corresponding to the to-be-detected image based on the spectrum image, performing copy attack detection on the to-be-detected image based on the first clue image, to obtain a copy attack detection result; The to-be-detected image performs living detection on the target object to obtain a living body detection result; a determining unit is configured to determine whether the target object is a living body based on the living body detection result and the remake attack detection result.

In one embodiment, the processing unit generates a first clue image corresponding to the to-be-detected image based on the spectral image: intercepts an image area including target pixels according to target rules; wherein, the target pixels For the pixel points located in each target pixel row and each target pixel column in the spectrum image, each of the target pixel rows is arranged at equal intervals, and each of the target pixel columns is arranged at equal intervals; splicing each of the image areas , to obtain the first clue image.

In one embodiment, the target pixel row is a pixel row other than the central pixel row of the spectral image, and the target pixel row is a pixel row other than the central pixel row of the spectral image.

In an embodiment, the target pixel row is located at a position corresponding to 1/8 or an integer multiple of 1/8 of the spectrum image, and the target pixel column is located at 1/8 or 1/8 of the spectrum image. at the position corresponding to an integer multiple of .

In one embodiment, the processing unit is further configured to: perform a brightness difference increase process on each of the image regions, where the brightness difference is a brightness difference between a bright pixel and other pixels in the image region.

In one embodiment, the processing unit performs the brightness difference increase processing on each of the image regions in the following manner: for each of the image regions, selects a specified number of first specified pixels in descending order of pixel values. pixel point, performing adjustment processing on the pixel value of the pixel point in the image area except the first designated pixel point; wherein, the adjustment processing includes setting the pixel value of the pixel point to the target value or adjusting The pixel value of the pixel is smaller than the pixel value of the pixel; or, for each of the image areas, determine a second specified pixel whose pixel value is less than the target pixel threshold, and perform adjustment processing on the pixel value of the second specified pixel; wherein, The adjustment process includes setting the pixel value of the pixel point to a target value or reducing the pixel value of the pixel point.

According to a fourth aspect of the embodiments of the present disclosure, there is provided an image detection apparatus, including:

The acquisition unit is used to acquire the image to be detected; the processing unit is used to perform frequency domain conversion on the image to be detected to obtain a spectrum image of the image to be detected, and generate a spectrum image corresponding to the image to be detected based on the spectrum image a first clue image; and for performing copy attack detection on the to-be-detected image based on the first clue image to obtain a copy attack detection result.

In one embodiment, the processing unit generates a first clue image corresponding to the image to be detected based on the spectrum image in the following manner: intercepting an image area including target pixels according to target rules; wherein, the The target pixel points are the pixel points located in each target pixel row and each target pixel column in the spectrum image, the target pixel rows are arranged at equal intervals, and the target pixel columns are arranged at equal intervals; The regions are spliced to obtain the first clue image.

In one embodiment, the target pixel row is a pixel row other than the central pixel row of the spectral image, and the target pixel row is a pixel row other than the central pixel row of the spectral image.

In an embodiment, the target pixel row is located at a position corresponding to 1/8 or an integer multiple of 1/8 of the spectrum image, and the target pixel column is located at 1/8 or 1/8 of the spectrum image. at the position corresponding to an integer multiple of .

According to a fifth aspect of the embodiments of the present disclosure, an electronic device is provided, including:

processor; memory for storing processor-executable instructions;

Wherein, the processor is configured to: execute the living body detection method described in the first aspect or any embodiment of the first aspect, or execute the image described in the second aspect or any embodiment of the second aspect Detection method.

According to a sixth aspect of the embodiments of the present disclosure, a storage medium is provided, where instructions are stored in the storage medium, and when the instructions in the storage medium are executed by a processor, the processor can execute the first aspect or the first aspect The living body detection method described in any one of the embodiments, or the image detection method described in the second aspect or any one of the embodiments of the second aspect.

According to a seventh aspect of the embodiments of the present disclosure, a computer program product is provided, the computer program product includes a computer program, and when the computer program is executed by a processor, the first aspect or any one of the implementation manners of the first aspect is implemented. The living body detection method, or the image detection method described in the second aspect or any one of the implementation manners of the second aspect is implemented.

The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: the image to be detected can be converted in the frequency domain to obtain a spectral image of the image to be detected, and a first clue image corresponding to the image to be detected can be generated from the spectral image. Further, the image to be detected can be subjected to remake attack detection through the first clue image to obtain a remake attack detection result, and the target object can be subjected to living body detection through the to-be-detected image to obtain the living body detection result. On this basis, it can be determined whether the target object is a living body through the living body detection result and the remake attack detection result. In this method, the remake attack detection is used as a supplementary detection of the living body detection, which can further improve the accuracy of the living body detection result. In addition, since this method detects the remake attack by using the clue image generated by the spectrogram of the image to be detected, the involved remake attack detection process is universal, and can realize the remake of the display image obtained by remaking the high-resolution screen. Accurate identification of images (that is, images without moiré or remake images with inconspicuous moiré patterns) enables the detection of remake attacks in high-definition screen remake scenarios, further improving the accuracy of live detection.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure.

Fig. 1 is a flow chart of a method for detecting a living body according to an exemplary embodiment.

Fig. 2 is a flowchart of a method for generating a first clue image corresponding to an image to be detected based on a spectral image according to an exemplary embodiment.

Fig. 3 is a schematic diagram of a target pixel in a spectrum image according to an exemplary embodiment.

Fig. 4 is a flowchart of another method for generating a first clue image corresponding to an image to be detected based on a spectral image according to an exemplary embodiment.

FIG. 5 is a schematic diagram of a clue image obtained by retaking an image after processing to increase the brightness difference, according to an exemplary embodiment.

Fig. 6 is a flow chart of another method for detecting a living body according to an exemplary embodiment.

Fig. 7 is a flow chart of an image detection method according to an exemplary embodiment.

Fig. 8 is a flowchart of a method for generating a first clue image corresponding to an image to be detected based on a spectral image according to an exemplary embodiment.

Fig. 9 is a block diagram of an apparatus for detecting a living body according to an exemplary embodiment.

Fig. 10 is a block diagram of an image detection apparatus according to an exemplary embodiment.

Fig. 11 is a block diagram of an electronic device for image detection or living body detection according to an exemplary embodiment.

Detailed ways

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. Where the following description refers to the drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the illustrative examples below are not intended to represent all implementations consistent with this disclosure.

Throughout the drawings, the same or similar reference numbers refer to the same or similar elements or elements having the same or similar functions. The described embodiments are some, but not all, of the embodiments of the present disclosure. The embodiments described below with reference to the accompanying drawings are exemplary, and are intended to explain the present disclosure and should not be construed as a limitation of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure. The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

In recent years, important progress has been made in the research of artificial intelligence-based computer vision, deep learning, machine learning, image processing, image recognition and other technologies. Artificial Intelligence (AI) is an emerging science and technology that studies and develops theories, methods, technologies and application systems for simulating and extending human intelligence. Artificial intelligence is a comprehensive discipline, involving chips, big data, cloud computing, Internet of Things, distributed storage, deep learning, machine learning, neural networks and many other types of technologies. As an important branch of artificial intelligence, computer vision is to let machines recognize the world. Computer vision technology usually includes face recognition, image detection, fingerprint recognition and anti-counterfeiting verification, biometric recognition, face detection, pedestrian detection, target detection, pedestrian detection Recognition, Image Processing, Image Recognition, Image Semantic Understanding, Image Retrieval, Text Recognition, Video Processing, Video Content Recognition, Behavior Recognition, 3D Reconstruction, Virtual Reality, Augmented Reality, Simultaneous Localization and Mapping (SLAM), Computational Photography, Robotics Navigation and positioning technology. With the research and progress of artificial intelligence technology, this technology has been applied in many fields, such as security, city management, traffic management, building management, park management, face access, face attendance, logistics management, warehouse management, robotics , smart marketing, computational photography, mobile imaging, cloud services, smart home, wearable devices, driverless, autonomous driving, smart medical care, face payment, face unlock, fingerprint unlock, witness verification, smart screen, smart TV, Cameras, mobile Internet, webcasting, beauty, beauty, medical beauty, intelligent temperature measurement and other fields.

The living body detection method provided by the embodiment of the present disclosure can be applied to a scene in which living body detection is performed by judging whether an image is a duplicated image.

With the development of computer technology, image detection technology has emerged. As an efficient and convenient verification method, it is widely used in many fields. In the related art, for example, in application scenarios such as living body detection and environmental analysis, it is usually necessary to discriminate whether the detected image is a duplicated image. Wherein, the re-shot image refers to an image obtained after the collected image is collected again. For example, an image obtained by retaking a picture displayed on the display screen by a mobile phone.

In the related art, it is possible to effectively detect the copied image obtained by copying the display screen by recognizing the moiré pattern. However, usually, only a display screen with a lower resolution (a display screen with a resolution less than or equal to 1280*x720) will make the remake image appear more obvious moiré. Therefore, in the related art, it is impossible to remake images obtained by reproducing a high-resolution display screen (such as a display screen with a resolution higher than 1280*720, such as a 4K display screen) (there is no moiré in the remake image, or the moiré in the remake image is This also makes it impossible for the living body detection scheme that uses related technologies to perform remake detection to achieve more accurate identification of the remake image.

In view of this, the present disclosure proposes a method for detecting a living body, which can perform frequency domain conversion on an image to be detected, obtain a spectral image of the image to be detected, and generate a clue image corresponding to the image to be detected through the spectral image. Further, the image to be detected can be subjected to remake attack detection through the clue image to obtain the remake attack detection result, and the target object can be subjected to living body detection through the to-be-detected image to obtain the living body detection result. On this basis, it can be determined whether the target object is a living body through the living body detection result and the remake attack detection result. In this method, the remake attack detection is used as a supplementary detection of the living body detection, which can further improve the accuracy of the living body detection result. In addition, since this method detects the remake attack by using the clue image generated by the spectrogram of the image to be detected, the involved remake attack detection process is universal, and can realize the remake of the display image obtained by remaking the high-resolution screen. Accurate identification of images (that is, images without moiré or remake images with inconspicuous moiré patterns) enables the detection of remake attacks in high-definition screen remake scenarios, further improving the accuracy of live detection. Hereinafter, for the convenience of description, the present disclosure will refer to the clue image obtained by the spectral image as the first clue image.

Fig. 1 is a flowchart of a method for detecting a living body according to an exemplary embodiment. As shown in Fig. 1 , the method includes the following steps.

In step S11, an image to be detected is acquired.

Wherein, the image to be detected includes the face area of the target object.

In step S12, frequency domain conversion is performed on the image to be detected to obtain a spectral image of the image to be detected, a first clue image corresponding to the image to be detected is generated based on the spectral image, and a copy attack detection is performed on the image to be detected based on the first clue image to obtain Remake the attack detection result, and perform living body detection on the target object based on the to-be-detected image to obtain the living body detection result.

In the embodiment of the present disclosure, the image to be detected is a three-channel image in a three-primary color (Red Green Blue, RGB) color space format, and a spectrum image obtained by performing frequency domain conversion on the image to be detected is a single-channel image. The single-channel data of the spectral image is the pixel value. For example, a fast Fourier transform (fast Fourier transform, fft) algorithm may be used to perform frequency domain transformation on the image to be detected.

In step S13, it is determined whether the target object is a living body based on the living body detection result and the remake attack detection result.

For example, for the detection result of the living body and the detection result of the remake attack, it can be determined whether the target object is a living body by means of "taking the intersection". For example, if the living body detection result is that the to-be-detected image is a living body image, and the duplication attack detection result is that the to-be-detected image is not a duplicated image, it is determined that the target object is a living body. If the living body detection result is that the to-be-detected image is a non-living body image, or the duplication attack detection result is that the to-be-detected image is a duplicated image, it is determined that the target object is a non-living body.

The liveness detection methods involved in the present disclosure may be, for example, motion liveness detection (such as blinking, mouth opening, etc.), colorful liveness detection, lip language liveness detection and other liveness detection methods, which are not specifically limited in this disclosure. In some specific implementation manners, for live detection and remake attack detection, one of the detection methods may be used to perform detection first, and if the detection is passed, the other detection method may be further performed. For example, the living body detection may be performed first through the image to be detected, and if the living body detection is passed, the remake attack detection may be performed on the first clue image. For another example, the first clue image may be used to perform the remake attack detection, and if the remake attack detection passes, the living body detection may be performed through the to-be-detected image. Of course, liveness detection and remake attack detection can also be performed simultaneously.

Generally, for a specified image and a replayed image obtained by performing a screen remake of the designated image, after the spectrum image obtained by spectrally transforming the two images respectively, there will be more obvious pixels for a specific area in the spectrum image. Arrange differences. In an embodiment of the present disclosure, the first clue image can be obtained by cropping and splicing the above-mentioned specific region.

Fig. 2 is a flowchart of a method for generating a first clue image corresponding to an image to be detected based on a spectral image according to an exemplary embodiment. As shown in Fig. 2 , the method includes the following steps.

In step S21, the image area including the target pixel is intercepted according to the target rule.

In the embodiment of the present disclosure, the target pixel point is the pixel intersection point located in each target pixel row and each target pixel column in the spectrum image. The target pixel rows are arranged at equal intervals, and the target pixel columns are arranged at equal intervals.

By way of example, the target pixel point may be used as the interception center point to intercept the image area, and other pixels close to the target pixel point may be used as the interception center point to intercept the image area. In addition, the interception range may be adjusted according to the actual situation of the spectrum image (for example, a pixel range of 20*20 may be used as the interception range), which is not specifically limited in the present disclosure.

In step S22, each image area is spliced to obtain a first clue image.

In the embodiment of the present disclosure, the image area including the target pixel is intercepted according to the target rule, and each image area is spliced to obtain the first clue image, in order to exclude the image area in the spectrum image that is not conducive to the detection of the remake attack. Retaining the image area that is prone to feature differences between the non-repeat image and the remake image can further improve the detection accuracy of the remake attack. With reference to FIG. 3 , the target pixel points selected in the spectrum image are exemplarily described below.

Fig. 3 is a schematic diagram of a target pixel in a spectrum image according to an exemplary embodiment. Exemplarily, as shown in FIG. 3 , the target pixel row may include, for example, pixel row X1, pixel row X2, pixel row X3, pixel row X4, pixel row X5, pixel row X6, and pixel row X7, and the target pixel column may, for example, include pixel rows. Column Y1, pixel column Y2, pixel column Y3, pixel column Y4, pixel column Y5, pixel column Y6, and pixel column Y7. The selected target pixel, for example, can be the intersection of each pixel marked with a square and a circle in FIG. Y4], [X1,Y5], [X1,Y6], [X1,Y7], [X2,Y1], [X2,Y2], [X2,Y3], [X2,Y4], [X2,Y5] , [X2, Y6], [X2, Y7], [X3, Y1], [X3, Y2], [X3, Y3], [X3, Y4], [X3, Y5], [X3, Y6], [ X3,Y7], [X4,Y1], [X4,Y2], [X4,Y3], [X4,Y4], [X4,Y5], [X4,Y6], [X4,Y7], [X5, Y1], [X5, Y2], [X5, Y3], [X5, Y4], [X5, Y5], [X5, Y6], [X5, Y7], [X6, Y1], [X6, Y2] , [X6,Y3], [X6,Y5], [X6,Y6], [X6,Y7], [X7,Y1], [X7,Y2], [X7,Y3], [X7,Y5], [ X7,Y6] and [X7,Y7].

Exemplarily, by comparing the spectrum image of the non-reproduced image with the spectrum image of the copied image, it is found that the center pixel row (the pixel row to which the center pixel of the spectrum image belongs) and the center pixel column (the pixel to which the center pixel of the spectrum image belongs) Pixel points near the column) usually do not have obvious differences in the arrangement of pixel values. In one embodiment, the pixel rows other than the central pixel row of the spectral image in the pixel rows arranged at equal intervals may be used as the target pixel row, and the pixel rows arranged at equal intervals except the central pixel row of the spectral image may be used as the target pixel row. The outer pixel column is used as the target pixel column.

Taking Figure 3 as an example, the central pixel row is pixel row X4, the central pixel column is pixel row Y4, and the target pixel point can be, for example, each pixel point marked with a square in Figure 3, specifically including pixel points [X1, Y1], [X1 ,Y2], [X1,Y3], [X1,Y5], [X1,Y6], [X1,Y7], [X2,Y1], [X2,Y2], [X2,Y3], [X2,Y5 ], [X2, Y6], [X2, Y7], [X3, Y1], [X3, Y2], [X3, Y3], [X3, Y5], [X3, Y6], [X3, Y7], [X5,Y1], [X5,Y2], [X5,Y3], [X5,Y5], [X5,Y6], [X5,Y7], [X6,Y1], [X6,Y2], [X6 ,Y3], [X6,Y5], [X6,Y6], [X6,Y7], [X7,Y1], [X7,Y2], [X7,Y3], [X7,Y5], [X7,Y6 ] and [X7, Y7] are determined as target pixels. After excluding the pixels belonging to the center pixel row or center pixel column, the obtained first clue image can better reflect the difference between the recapped image and the non-recaptured image, which can further improve the detection effect of recapture attacks.

In addition, in the actual detection process of the remake attack, it was found that the target pixel row and target pixel column should be located at 1/8 or an integer multiple of 1/8 of the spectral image (specifically including 2/8, 3/8, 4/8). , 5/8, 6/8, 7/8), the selected target pixel will fall into the image area in the spectrum image that is easy to identify the remake image. Among them, as shown in Figure 3, the pixel at the 1/8 position of the spectrum image is the pixel row X1, the pixel at the 2/8 position of the spectrum image is the pixel row X2, and the pixel at the 3/8 position of the spectrum image is the row X2. Pixel row X3. In addition, correspondingly, the pixel row X4 to the pixel row X7 are respectively the pixel row located at the 4/8 position of the spectrum image to the pixel row located at the 7/8 position of the spectrum image, which will not be repeated in the present disclosure. The spectral image can be divided into 8 image areas of equal size by dividing the spectral image by the target pixel row. Similarly, the pixel column at the 1/8 position of the spectrum image is the pixel column Y1, the pixel at the 2/8 position of the spectrum image is the pixel column Y2, and the pixel at the 3/8 position of the spectrum image is the pixel column. Y3. In addition, correspondingly, the pixel row Y4 to the pixel row Y7 are respectively the pixel row located at the 4/8 position of the spectrum image to the pixel row located at the 7/8 position of the spectrum image, which will not be described in detail herein. The spectral image is divided into column regions by the target pixel column, and the spectral image can be divided into 8 image regions of equal size. On this basis, the pixel row located at the position of 1/8 or an integer multiple of 1/8 of the spectrum image is used as the target pixel row, and the pixel located at the position of 1/8 or an integer multiple of 1/8 of the spectrum image is used as the target pixel row. The column is used as the target pixel column, and the central pixel row (the pixel row located at the 4/8 position of the spectral image) and the central pixel column (the pixel located at the 4/8 position of the spectral image) are removed from the target pixel row and target pixel column. column), and finally, multiple image areas that are easy to identify the remake image can be obtained, and the multiple image areas obtained by this method are spliced to obtain the first clue image, which can be used as a preferred method for obtaining the first clue image in the present disclosure. Way.

For example, in the process of splicing multiple image regions, the spliced positions of each image region are consistent with their original positions in the spectrum image. For example, as shown in Figure 3, for the image area obtained with the pixel point [X1, Y1] as the center point, the corresponding image area is still located in the upper left position in the spliced image, for the pixel point [X7, Y7] as the The image area obtained from the center point, the corresponding image area is still located in the lower right position in the stitched image.

With the image detection method provided by the embodiment of the present disclosure, an image area that is helpful for recognizing the remake image can be screened out in the spectrum image. Further, by cropping and splicing the designated image area, and performing remake attack detection on the first clue image obtained by splicing, a relatively accurate remake attack detection result can be obtained.

Generally, although there is a difference in the spectral arrangement between the clue image obtained from the non-repeat image and the clue image obtained from the retake image, the difference is not obvious. If the remake attack detection is directly performed on the first clue image, the detection accuracy is limited. In view of this, in the embodiment of the present disclosure, brightness difference increasing processing may be performed on each image area, where the brightness difference is the brightness difference between the bright pixel and the remaining pixels in the image area. The difference in pixel value between the remake image and the non-remake image can be highlighted by the brightness difference increasing process. Among them, for the spectral image, the pixel value of the pixel can represent the brightness of the pixel, and the higher the pixel value, the brighter the pixel. The following is an exemplary description of an embodiment of the luminance difference increasing process by the image area.

Specifically, the process of increasing the brightness difference on the image areas may be performed before splicing each image area to generate the first clue image, or may be performed after splicing each image area to generate the first clue image; this The disclosed embodiments do not limit the specific execution time of the above brightness difference increasing processing.

FIG. 4 is a flowchart of another method for generating a first clue image corresponding to an image to be detected based on a spectral image according to an exemplary embodiment. As shown in FIG. 4 , step S31 in the embodiment of the present disclosure is the same as that in FIG. 2 . The execution method of step S21 in the above is similar, which is not repeated here.

In step S32, luminance difference increasing processing is performed on each image area.

Among them, the brightness difference is the brightness difference between the bright pixel and the remaining pixels in the image area.

In step S33, each image area after the brightness difference increasing process is stitched to obtain a first clue image.

In the embodiment of the present disclosure, by performing brightness difference increasing processing on a plurality of different image regions, the clue image difference between the remake image and the non-remake image can be further enlarged, so that the remake attack detection accuracy can be further improved.

The present disclosure exemplifies possible implementations of the luminance difference increase process below. Hereinafter, for the convenience of description in the present disclosure, the specified pixel point selected in the first manner is referred to as the first specified pixel point, and the specified pixel point selected in the second manner is referred to as the second specified pixel point.

Mode 1: For each image area, select a specified number of first designated pixels in descending order of pixel values, and adjust the pixel values of pixels in the image area other than the first designated pixels.

For example, the specified number may be 20, of course, it may be other, the specific value of the above specified number can be set according to actual needs, and the embodiment of the present disclosure does not limit the specific value of the above specified number.

Method 2: For each image area, determine a second designated pixel whose pixel value is smaller than the target pixel threshold, and perform adjustment processing on the pixel value of the second designated pixel.

In addition, for the above-mentioned manner 1 or manner 2, the adjustment process may include, for example, setting the pixel value of the pixel point to a target value (for example, the target value may be 0), or reducing the corresponding pixel point (for manner 1, the adjusted pixel value The point is a pixel point other than the first designated pixel point, and for the second method, the adjusted pixel point is the pixel value of the second designated pixel point).

In the above-mentioned embodiment, the method of increasing the brightness difference of each image area is aimed at further improving the difference of clue images between the duplicated image and the non-duplicated image, so as to further improve the detection accuracy of the duplicated attack.

FIG. 5 is a schematic diagram of a clue image obtained by retaking an image after processing to increase the brightness difference, according to an exemplary embodiment. Exemplarily, as shown in FIG. 5 , the black area in the clue image corresponds to the pixels adjusted during the process of increasing the brightness difference, and the white area corresponds to the pixels that were not adjusted during the process of increasing the brightness difference. Generally, for the clue image obtained by the non-reproduced image after the brightness difference increase processing, the pixels located in the white area in the image have no obvious arrangement pattern, while the brightness difference obtained by the duplicate image is increased. In the image, the pixel low points in the white area in the image have obvious arrangement rules (specifically, as shown in Figure 5, the pixels that are not adjusted in the process of increasing the brightness difference will be concentrated and regularly arranged in the clue image. cloth).

Exemplarily, a pre-trained target detection model may be used to detect a remake attack on the first clue image.

FIG. 6 is a flowchart of another method for detecting a living body according to an exemplary embodiment. As shown in FIG. 6 , steps S41 and S43 in the embodiment of the present disclosure are similar to the execution methods of steps S11 and S13 in FIG. 1 . , which will not be repeated here.

In step S42, frequency domain conversion is performed on the image to be detected to obtain a spectral image of the image to be detected, a first clue image corresponding to the image to be detected is generated based on the spectral image, the first clue image is input into the target detection model, and the target detection model is obtained Based on the output information, a remake attack detection result of the image to be detected is obtained based on the output information, and a living body detection result is obtained on the target object based on the to-be-detected image.

The target detection model is obtained by training the second clue image corresponding to the non-remake image and the third clue image corresponding to the remake image. In addition, the recapped image includes, for example, an image obtained by photographing a picture displayed on a screen with a resolution higher than a specified resolution (for example, the specified resolution may be 1280*720).

Illustratively, the target detection model may be, for example, a neural network model with a very deep convolutional network (Very Deep Convolutional Networks, VGG) structure.

In the embodiment of the present disclosure, the target detection model is used to determine whether the to-be-detected image is a remake image based on the spectral features of the clue image. For example, the clue image can be input into the target detection model, and the remake attack detection result can be obtained according to the output information. The remake detection result may be, for example, a probability value that the image to be detected is a remake image, and of course, may also be a final discrimination result obtained by performing binary classification according to the probability value. (For example, "The image to be detected is a remake" or "The image to be detected is not a remake")

Illustratively, the target probability value (an example is represented by 0.5) can be used to discriminate the remake detection result. If the target detection model determines that the probability value of the image to be detected is a remake image is greater than the target probability value, “1” is output, indicating the to-be-detected image. Image is a remake. If the target detection model determines that the probability value of the image to be detected is a retaken image is less than or equal to the target probability value, "0" is output, indicating that the image to be detected is not a retaken image.

In addition, by cropping the background area of the retaken image and retaining the face area of the retaken image, the interference of the background area in the image to the training of the target detection model can be reduced, so as to ensure the training effect of the model. Wherein, the face region of the target object in the retaken image may be determined by, for example, a face detection algorithm. Further, the face region determined by the face detection algorithm can also be adjusted (increasing or reducing the range of the face region) based on manual adjustment, so as to obtain a cropped image that only includes the face region of the target object.

In addition, in order to further improve the feature learning efficiency of the target detection model, two-channel position encoding can be configured for the second clue image and the third clue image. Among them, the position code is used to identify the position of each pixel in the image, taking the position code [X, Y] as an example, X is used to identify the pixel row, and Y is used to identify the pixel column. Further, channel splicing can be performed on the two-channel position encoding of the second clue image (or the third clue image) and the single-channel data (ie, pixel values) of the second clue image (or the third clue image) to obtain a The second cue image (or the third cue image) for the three-channel data. Further, the target detection model can be trained by the second clue image with three-channel data and the third clue image with three-channel data, and then stop when the target detection model converges or the number of training steps of the target detection model reaches a specified number of steps. Train to get the target detection model.

In the living body detection method provided by the embodiments of the present disclosure, it is possible to judge whether the image to be detected is a duplicated image based on the spectral characteristics of the image to be detected. Compared with the conventional method of detecting reprinted images through moiré included in the image, the image detection method proposed in the present disclosure has universality, and still has a high recognition ability for reprinted images with no moiré or inconspicuous moiré. Accuracy, can meet the detection needs of high-resolution display re-shot images. On this basis, the detection accuracy of living body can be further improved by combining the attack detection of remakes with the detection of living bodies. This method can achieve accurate detection for, for example, the attack images of remakes of high-resolution display screens.

In addition, the present disclosure is based on the same concept, and an embodiment of the present disclosure also provides an image detection method, which can realize the detection of a duplicated image, so as to meet the duplicated image detection requirements of application scenarios such as living body detection and environmental analysis.

Fig. 7 is a flowchart of an image detection method according to an exemplary embodiment. As shown in Fig. 7 , the method includes the following steps.

In step S51, an image to be detected is acquired.

In step S52, frequency domain conversion is performed on the image to be detected to obtain a spectral image of the image to be detected, and a first clue image corresponding to the image to be detected is generated based on the spectral image.

In step S53, the image to be detected is subjected to remake attack detection based on the first clue image to obtain a remake attack detection result.

In one embodiment, the first clue image can be obtained by cropping and splicing a specific area in the first spectral image.

Fig. 8 is a flowchart of a method for generating a first clue image corresponding to an image to be detected based on a spectral image according to an exemplary embodiment. As shown in Fig. 8 , the method includes the following steps.

In step S61, the image area including the target pixel is intercepted according to the target rule.

In the embodiment of the present disclosure, the target pixel point is the pixel intersection point located in each target pixel row and each target pixel column in the spectrum image. The target pixel rows are arranged at equal intervals, and the target pixel columns are arranged at equal intervals.

By way of example, the target pixel point may be used as the interception center point to intercept the image area, and other pixels close to the target pixel point may be used as the interception center point to intercept the image area. In addition, the interception range may be adjusted according to the actual situation of the spectrum image (for example, a pixel range of 20*20 may be used as the interception range), which is not specifically limited in the present disclosure.

In step S62, each image area is spliced to obtain a first clue image.

In the embodiment of the present disclosure, the image area including the target pixel is intercepted according to the target rule, and the image areas are spliced to obtain the first clue image, which can retain the image that is prone to feature differences between the non-remake image and the remake image. region, which can improve the detection accuracy of remake attacks.

In addition, for example, a pixel row located at a position of 1/8 or an integer multiple of 1/8 of the spectrum image may be used as a target pixel row, and a pixel located at a position of 1/8 or an integer multiple of 1/8 of the spectrum image may be used as the target pixel row. The column is used as the target pixel column, and the central pixel row and the central pixel column are eliminated from the target pixel row and target pixel column, and finally multiple image areas that are easy to identify the remake image can be obtained. Performing splicing to obtain the first clue image can be used as a preferred method for obtaining the first clue image in the present disclosure.

The copying attack detection method involved in the embodiments of the present disclosure is similar to the copying attack detection method involved in the above-mentioned living body detection method. For the unclear contents of the image detection method involved in the present disclosure, you can directly refer to the method involved in the living body detection method. any of the examples.

Based on the same concept, an embodiment of the present disclosure also provides a living body detection device.

It can be understood that, in order to implement the above-mentioned functions, the image detection apparatus provided by the embodiments of the present disclosure includes corresponding hardware structures and/or software modules for executing each function. Combining with the units and algorithm steps of each example disclosed in the embodiments of the present disclosure, the embodiments of the present disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is performed by hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present disclosure.

Fig. 9 is a block diagram of an apparatus for detecting a living body according to an exemplary embodiment. Referring to FIG. 9 , the apparatus 100 includes an acquisition unit 101 , a processing unit 102 and a determination unit 103 .

The acquiring unit 101 is used for acquiring the image to be detected. Wherein, the image to be detected includes the face area of the target object. The processing unit 102 is configured to perform frequency domain conversion on the image to be detected, obtain a spectral image of the image to be detected, generate a first clue image corresponding to the image to be detected based on the spectral image, and perform copy attack detection on the image to be detected based on the first clue image, Get the remake attack detection result. And it is used to perform live detection on the target object based on the image to be detected to obtain the live detection result. The determining unit 103 is configured to determine whether the target object is a living body based on the living body detection result and the remake attack detection result.

In an embodiment, the processing unit 102 generates a first clue image corresponding to the image to be detected based on the spectrum image: intercepts an image area including the target pixel point according to the target rule. The target pixel points are pixels located in each target pixel row and each target pixel column in the spectrum image, each target pixel row is arranged at equal intervals, and each target pixel column is arranged at equal intervals. Each image area is spliced to obtain the first clue image.

In one embodiment, the target pixel row is a pixel row other than the central pixel row of the spectral image, and the target pixel row is a pixel row other than the central pixel row of the spectral image.

In one embodiment, the target pixel row is located at a position corresponding to 1/8 or an integer multiple of 1/8 of the spectral image, and the target pixel column is located at a position corresponding to 1/8 or an integer multiple of 1/8 of the spectral image.

In one embodiment, the processing unit 102 is further configured to: perform brightness difference increasing processing on each image area, where the brightness difference is the brightness difference between the bright pixel and the remaining pixels in the image area.

In an embodiment, the processing unit 102 performs the brightness difference increase processing on each image area in the following manner: for each image area, selects a specified number of first specified pixels in the order of pixel values from large to small, The pixel values of the pixel points in the area other than the first designated pixel point are adjusted. The adjustment processing includes setting the pixel value of the pixel point to a target value or reducing the pixel value of the pixel point. Or, for each image area, determine a second designated pixel whose pixel value is smaller than the target pixel threshold, and perform adjustment processing on the pixel value of the second designated pixel. The adjustment processing includes setting the pixel value of the pixel point to a target value or reducing the pixel value of the pixel point.

Based on the same concept, the present disclosure also proposes an image detection device.

Fig. 10 is a block diagram of an image detection apparatus according to an exemplary embodiment. Referring to FIG. 10 , the apparatus 200 includes an acquisition unit 201 and a processing unit 202 .

The acquiring unit 201 is used for acquiring the image to be detected. The processing unit 202 performs frequency domain conversion on the image to be detected to obtain a spectral image of the image to be detected, and generates a first clue image corresponding to the image to be detected based on the spectral image. Based on the first clue image, the image to be detected is subjected to remake attack detection to obtain a remake attack detection result.

In one embodiment, the processing unit 202 generates the first clue image corresponding to the image to be detected based on the spectrum image in the following manner: intercepting the image area including the target pixel point according to the target rule. The target pixel points are pixels located in each target pixel row and each target pixel column in the spectrum image, each target pixel row is arranged at equal intervals, and each target pixel column is arranged at equal intervals. Each image area is spliced to obtain the first clue image.

In one embodiment, the target pixel row is a pixel row other than the central pixel row of the spectral image, and the target pixel row is a pixel row other than the central pixel row of the spectral image.

In one embodiment, the target pixel row is located at a position corresponding to 1/8 or an integer multiple of 1/8 of the spectral image, and the target pixel column is located at a position corresponding to 1/8 or an integer multiple of 1/8 of the spectral image.

Regarding the apparatus in the above-mentioned embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be described in detail here.

FIG. 11 is a block diagram of an electronic device 300 for image detection or living body detection according to an exemplary embodiment.

As shown in FIG. 11 , an embodiment of the present disclosure provides an electronic device 300 . The electronic device 300 includes a memory 301 , a processor 302 , and an input/output (I/O) interface 303 . Among them, the memory 301 is used for storing instructions. The processor 302 is configured to invoke the instructions stored in the memory 301 to execute the image detection or living body detection method according to the embodiment of the present disclosure. Wherein, the processor 302 is respectively connected with the memory 301 and the I/O interface 303, for example, it can be connected through a bus system and/or other forms of connection mechanisms (not shown). The memory 301 can be used to store programs and data, including the programs of the image detection or living body detection methods involved in the embodiments of the present disclosure, and the processor 302 executes various functional applications and data processing of the electronic device 300 by running the programs stored in the memory 301 .

In this embodiment of the present disclosure, the processor 302 may use at least one of a digital signal processor (Digital Signal Processing, DSP), a field programmable gate array (Field Programmable Gate Array, FPGA), and a programmable logic array (Programmable Logic Array, PLA). It is implemented in a hardware form, and the processor 302 may be a central processing unit (Central Processing Unit, CPU) or one or a combination of other forms of processing units with data processing capability and/or instruction execution capability.

The memory 301 in the embodiments of the present disclosure may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, a random access memory (Random Access Memory, RAM) and/or a cache memory (cache). The non-volatile memory may include, for example, a read only memory (Read Only Memory, ROM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, HDD), or a solid state drive (Solid State Drive, SSD), and the like.

In the embodiment of the present disclosure, the I/O interface 303 can be used to receive input instructions (such as numeric or character information, and generate key signal input related to user settings and function control of the electronic device 300 , etc.), and can also output various external information (for example, images or sounds, etc.). In this embodiment of the present disclosure, the I/O interface 303 may include one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a mouse, a joystick, a trackball, a microphone, a speaker, and a touch panel, etc. indivual.

In some embodiments, the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform any of the methods described above .

In some embodiments, the present disclosure provides a computer program product comprising a computer program that, when executed by a processor, performs any of the methods described above.

Although operations are depicted in the figures in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown, or in a serial order, or that all operations shown be performed to obtain desirable results . In certain circumstances, multitasking and parallel processing may be advantageous.

The methods and apparatus of the present disclosure can be accomplished using standard programming techniques, using rule-based logic or other logic to implement the various method steps. It should also be noted that the terms "means" and "module" as used herein and in the claims are intended to include implementations using one or more lines of software code and/or hardware implementations and/or means for receiving input.

Any steps, operations or procedures described herein may be performed or implemented using one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented using a computer program product comprising a computer-readable medium containing computer program code executable by a computer processor for performing any or all of the described steps, operations or procedures.

The foregoing descriptions of implementations of the present disclosure have been presented for the purposes of illustration and description. The foregoing description is not intended to be exhaustive nor to limit the present disclosure to the precise forms disclosed, and various variations and modifications are possible in light of the above teachings or may be obtained from practice of the present disclosure. The embodiments were chosen and described in order to explain the principles of the disclosure and its practical application to enable others skilled in the art to utilize the disclosure in various embodiments and with various modifications as are suited to the particular use contemplated.

Regarding the apparatus in the above-mentioned embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be described in detail here.

It should be understood that in the present disclosure, "plurality" refers to two or more than two, and other quantifiers are similar. "And/or", which describes the association relationship of the associated objects, means that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, A and B exist at the same time, and B exists alone. The character "/" generally indicates that the associated objects are an "or" relationship. The singular forms "a," "the," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise.

It is further understood that the terms "first", "second", etc. are used to describe various information, but the information should not be limited to these terms. These terms are only used to distinguish the same type of information from one another, and do not imply a particular order or level of importance. In fact, the expressions "first", "second" etc. are used completely interchangeably. For example, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information, without departing from the scope of the present disclosure.

It should be further understood that, unless otherwise specified, "connection" includes a direct connection between the two without other components, and also includes an indirect connection between the two with other elements.

It is further to be understood that, although the operations in the embodiments of the present disclosure are described in a specific order in the drawings, it should not be construed as requiring that the operations be performed in the specific order shown or the serial order, or requiring Perform all operations shown to obtain the desired result. In certain circumstances, multitasking and parallel processing may be advantageous.

Other embodiments of the present disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or techniques in the technical field not disclosed by the present disclosure . The specification and examples are to be regarded as exemplary only, with the true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise structures described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the scope of the appended rights.

Claims (15)

1. A method of in vivo detection, the method comprising:

acquiring an image to be detected; wherein the image to be detected comprises a face area of a target object;

performing frequency domain conversion on the image to be detected to obtain a frequency spectrum image of the image to be detected, generating a first line image corresponding to the image to be detected based on the frequency spectrum image, and performing copying attack detection on the image to be detected based on the first line image to obtain a copying attack detection result; performing living body detection on the target object based on the image to be detected to obtain a living body detection result;

and determining whether the target object is a living body or not based on the living body detection result and the copying attack detection result.

2. The in-vivo detection method according to claim 1, wherein said generating a first cable image corresponding to the image to be detected based on the spectrum image comprises:

intercepting an image area containing a target pixel point according to a target rule; the target pixel points are pixel intersection points of target pixel rows and target pixel columns in the frequency spectrum image, the target pixel rows are arranged at equal intervals, and the target pixel columns are arranged at equal intervals;

and splicing the image areas to obtain the first cable image.

3. The living body detection method according to claim 2, wherein the target pixel row is a pixel row other than a center pixel row of the spectral image, and the target pixel column is a pixel column other than the center pixel column of the spectral image.

4. The living body detecting method according to claim 2 or 3, wherein the target pixel row is located at a position corresponding to an integer multiple of 1/8 or 1/8 of the spectral image, and the target pixel column is located at a position corresponding to an integer multiple of 1/8 or 1/8 of the spectral image.

5. The in-vivo detection method according to any one of claims 2 to 4, wherein the method further comprises:

and performing brightness difference increasing processing on each image area, wherein the brightness difference is the brightness difference between the bright point pixel and the rest pixels in the image area.

6. The living body detection method according to claim 5, wherein the performing of the brightness difference increasing process on each of the image areas includes:

aiming at each image area, selecting a specified number of first specified pixel points according to the sequence of pixel values from large to small, and adjusting the pixel values of the pixel points in the image area except the first specified pixel points; the adjustment processing comprises setting the pixel value of the pixel point to be a target value or reducing the pixel value of the pixel point;

or,

for each image area, determining a second designated pixel point with a pixel value smaller than a target pixel threshold value, and adjusting the pixel value of the second designated pixel point; and adjusting the pixel value of the pixel point to be a target value or to be reduced.

7. The in-vivo detection method according to any one of claims 1 to 6, wherein the performing of the detection of the copying attack on the image to be detected based on the first line image to obtain a result of the detection of the copying attack comprises:

inputting the first cable image into a target detection model, acquiring output information of the target detection model, and obtaining a copying attack detection result of the image to be detected based on the output information; the target detection model is obtained by training based on a second cable image corresponding to the non-reproduction image and a third cable image corresponding to the reproduction image.

8. The biopsy method according to claim 7, wherein the reproduced image is an image obtained by photographing a screen displayed at a resolution higher than a predetermined resolution.

9. An image detection method, comprising:

acquiring an image to be detected;

performing frequency domain conversion on the image to be detected to obtain a frequency spectrum image of the image to be detected, and generating a first cable image corresponding to the image to be detected based on the frequency spectrum image;

and carrying out copying attack detection on the image to be detected based on the first line image to obtain a copying attack detection result.

10. The image detection method according to claim 9, wherein the generating a first cable image corresponding to the image to be detected based on the spectrum image comprises:

intercepting an image area containing a target pixel point according to a target rule; the target pixel points are pixel points which are positioned in each target pixel row and each target pixel column in the frequency spectrum image, each target pixel row is arranged at equal intervals, and each target pixel column is arranged at equal intervals;

and splicing the image areas to obtain the first cable image.

11. The image detection method according to claim 10, wherein the target pixel row is a pixel row other than a center pixel row of the spectrum image, and the target pixel column is a pixel column other than the center pixel column of the spectrum image.

12. The image detection method according to claim 10 or 11, wherein the target pixel row is located at a position corresponding to an integer multiple of 1/8 or 1/8 of the spectral image, and the target pixel column is located at a position corresponding to an integer multiple of 1/8 or 1/8 of the spectral image.

13. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to: performing the in-vivo detection method of any one of claims 1 to 8, or performing the image detection method of any one of claims 9 to 12.

14. A storage medium having stored therein instructions that, when executed by a processor, enable the processor to perform the liveness detection method of any one of claims 1 to 8 or the image detection method of any one of claims 9 to 12.

15. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, implements the living body detection method of any one of claims 1 to 8 or performs the image detection method of any one of claims 9 to 12.

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