CN112861764A - Face recognition living body judgment method - Google Patents
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Abstract
The invention discloses a face recognition living body judgment method, which comprises the following steps: the laser projector projects laser onto the face to be recognized, and the laser forms speckles on the face to be recognized; the infrared camera acquires a speckle image of a face to be recognized and sends the speckle image of the face to be recognized to the recognition and judgment unit; the identification and judgment unit identifies a face area in the speckle image after receiving the speckle image sent by the infrared camera; extracting images of all speckles in the face area; and comparing all the extracted speckle images with the standard speckle images one by one, calculating the similarity between each speckle image and the standard speckle image, then calculating the mean value of the similarity of all the speckle images, taking the mean value of the similarity as a living body recognition score, and if the living body recognition score is larger than a set threshold value, considering that the face to be recognized is a living body face. The invention can effectively distinguish the living body face from the non-living body face by utilizing the absorption characteristic of the laser speckles on the skin of the face, and has simple algorithm.
Description
Technical Field
The invention belongs to the technical field of face recognition, and particularly relates to a method for judging whether a living body exists in a face recognition process.
Background
With the development and popularization of face recognition technology, face recognition technology is required to be used for recognizing the identity of a person in more and more scenes. However, some lawbreakers can use pictures or videos or simulation head models to replace real people to carry out face recognition, so that the face recognition system has potential safety hazards. And the living body detection is carried out in the face recognition process, so that whether the face currently recognized is a living body face, a face in a photo or a video or a simulation head model can be judged, and the safety of the face recognition system is ensured. At present, the living body detection in the face recognition process is mainly based on the information of an RGB camera, an infrared camera or a depth camera for recognition, but although the living body detection can be realized to a certain extent by the living body judgment technologies, the living body judgment technologies have defects, for example, the technology for carrying out the living body detection on the face of a 2D image such as an RGB image or an infrared image has insufficient precaution capacity on high-definition photos and videos; the 3D image obtained by the depth camera is used for the biopsy, which can prevent the attack of the photo and the video, but cannot prevent the attack of the simulation head model.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method for judging a living body in face recognition based on laser speckle.
The purpose of the invention is realized by adopting the following technical scheme:
a face recognition living body judgment method comprises the following steps:
s1, the laser projector projects laser onto the face to be recognized, and the laser forms speckles on the face to be recognized;
s2, the infrared camera acquires the speckle images of the face to be recognized and sends the speckle images of the face to be recognized to the recognition and judgment unit;
s3, after receiving the speckle images sent by the infrared camera, the identification and judgment unit identifies the face area in the speckle images;
s4, extracting images of all speckles in the face area;
and S5, comparing all the extracted speckle images with the standard speckle images one by one, calculating the similarity between each speckle image and the standard speckle images, then calculating the mean value of the similarity of all the speckle images, taking the mean value of the similarity as a living body recognition score, and if the living body recognition score is larger than a set threshold value, considering that the face to be recognized is a living body face.
Further, in step S4, when an image of a certain speckle is extracted from the face region, the sub-pixel coordinates of the center of the speckle are determined, and an image of a region of a predetermined size centered on the center of the speckle is extracted.
Furthermore, the Euclidean distance is adopted to represent the similarity between a certain speckle image and a standard speckle image.
Further, the similarity between a certain speckle image and the standard speckle image is equal to the negative value of the Euclidean distance between the speckle image and the standard speckle image.
Further, the standard speckle images are obtained by projecting laser onto different faces at different angles and different distances by using a laser projector in different scenes, respectively performing speckle extraction on face regions of the face images after the face images are acquired by an infrared camera, and calculating an average image of all the extracted speckle images, namely the standard speckle image.
Further, after step S4, the depth of all speckle images is obtained by the depth camera, and in step S5, all speckle images with depth information are compared with the standard speckle images corresponding to the depth of the standard speckle image set one by one, the similarity between the speckle images and the corresponding standard speckle images is calculated, and then the mean value of the similarity between all speckle images is calculated as the living body identification score.
Furthermore, the standard speckle image set comprises a plurality of standard speckle images, each standard speckle image represents a certain depth interval, when similarity comparison is carried out, the standard speckle images corresponding to the depth intervals are selected from the standard speckle image set for comparison according to the depth values of the speckle images, and the similarity between the speckle images and the standard speckle images of the depth intervals is calculated.
Further, the standard speckle images in the standard speckle image set are obtained according to the following method: the method comprises the steps of projecting laser to different faces at different angles and different distances by using a laser projector under different scenes, obtaining face images by using an infrared camera, performing speckle extraction on face areas of the face images respectively to obtain images of all speckles in the face areas, obtaining depth values of the speckle images by using a depth camera, dividing the speckle images with different depth values into corresponding depth intervals according to the depth values, calculating an average image of all the speckle images in each depth interval subset, namely a standard speckle image of the depth interval subset, and storing the average image into a standard speckle image set.
Compared with the prior art, the invention has the beneficial effects that: the invention distinguishes living bodies or non-living bodies by utilizing the different absorption characteristics of laser speckles on living body skin and non-living body skin, forms speckles on a face to be identified through a laser projector and obtains all speckle images of a face area in the face identification process, calculates the similarity of the speckle images and a standard speckle image, and judges whether the face is the living body face by taking the average value of the similarity of all the speckle images as a living body identification score. In the preferred technical scheme, the depth value of the speckles is acquired while the speckles are extracted through the depth camera, the depth information is introduced into the standard speckle image, an image set containing standard speckle images in different depth intervals is established, the depth information is introduced during speckle comparison operation, the speckles can be compared with the standard speckles with similar depths, and robustness and accuracy are greatly improved.
Drawings
FIG. 1 is a flowchart of example 1 of the present invention;
FIG. 2 is an exemplary diagram of a speckle image;
FIG. 3 is a flowchart of example 2 of the present invention;
fig. 4a and 4b are exemplary views of speckle images at different depths, respectively.
Detailed Description
The present invention will be further described with reference to the following embodiments.
When laser irradiates an optical rough surface, a formed reflected light field has random spatial light intensity distribution and a granular structure due to the coherent superposition result of wavelets scattered by a large number of irregularly distributed surface elements on the surface, which is called as a laser speckle effect. The absorption of human skin to laser speckles has obvious characteristics, and the absorption characteristics of laser on human faces and the absorption characteristics on non-living body surfaces have obvious differences. In the process of face recognition, a laser projector is used for projecting laser on a face to be recognized, the laser wavelength can be 850nm or 940nm, laser speckles are formed on the face to be recognized by the laser, then an infrared camera is used for acquiring speckle images of the face to be recognized, and the speckle images are sent to a recognition judgment unit for living body recognition. The living skin has special absorptivity to speckle, so that the human skin can be distinguished from photos, videos, head models or other attack props. The method of the present invention will be described in detail below with reference to the accompanying drawings.
Example 1
Fig. 1 is a flowchart of an embodiment of the present invention, and as shown in fig. 1, the living body judgment method for face recognition of the present embodiment includes the following steps:
s1, the laser projector projects laser onto the face to be recognized, and the laser forms speckles on the face to be recognized;
s2, the infrared camera acquires the speckle images of the face to be recognized and sends the speckle images of the face to be recognized to the recognition and judgment unit; as shown in fig. 2, one frame of image acquired by the infrared camera has a plurality of speckles thereon;
s3, after receiving the speckle images sent by the infrared camera, the recognition and judgment unit detects the face regions of the images and recognizes the face regions in the speckle images, the recognition of the face regions can adopt the existing methods, such as a face detection method based on a neural network, such as YOLO3, MCNN, Fast-MCNN and the like, and the method for recognizing the face regions is not an innovative part of the invention and is not described in detail here;
s4, extracting images of all speckles in a face area in the image, namely extracting each speckle in the face area, wherein the speckle extraction is to determine the sub-pixel coordinates of a speckle center, the extracted speckle image is an image of an area with a set size with the speckle (speckle center) as the center, the size of the extracted area (image) can be set according to requirements, and the speckle image can be extracted by using a corrosion expansion algorithm;
and S5, comparing all the extracted speckle images with the standard speckle images one by one, calculating the similarity between each speckle image and the standard speckle image, then calculating the mean value of the similarity of all the speckle images, taking the mean value of the similarity as a living body recognition score, and if the living body recognition score is larger than a set threshold value, considering that the face to be recognized is a living body face. The threshold value is set to an empirical value, and may be set in accordance with a desired recognition rate and a human passing rate. For example, a test set with a large number, balanced positive and negative samples and wide scene coverage is preset, and the threshold is determined by considering the recognition rate and the real person passing rate simultaneously on the test set. The similarity between the speckle images and the standard speckle images is represented by Euclidean distance, the smaller the Euclidean distance is, the smaller the image difference is, the larger the similarity is, the larger the living body identification score is, the more the living body is, the judgment rule of the invention is that the similarity is equal to the negative value of the Euclidean distance between the speckle images and the standard speckle images, namely, the Euclidean distance is multiplied by negative one.
The standard speckle images of the invention are obtained by projecting laser to different faces at different angles and different distances by using a laser projector under different scenes, then acquiring the face images by using an infrared camera, then respectively carrying out speckle extraction on the face areas of the face images, and then calculating the average images (image mean value) of all the speckle images obtained by extraction, wherein the average images of all the speckle images are the standard speckle images.
Example 2
Fig. 3 is a flowchart of the present embodiment, and as shown in fig. 3, the present embodiment is different from embodiment 1 in that: after images of all speckles are extracted from a face area, the method further comprises a step of obtaining the depth of the speckles, in the step, a depth camera is used for obtaining the depth of all the speckle images, then the speckle images with depth information are compared with standard speckle images in corresponding depth intervals in a standard speckle image set one by one, the similarity is calculated, and then the mean value of the similarity of all the speckle images is used as a living body identification score. The standard speckle image set of the embodiment includes a plurality of standard speckle images with depth information, the depth values of each standard speckle image are different, and when similarity comparison is performed, the standard speckle image set selects the standard speckle image in the corresponding depth interval to perform comparison according to the depth value of the speckle image, and the similarity between the speckle image and the standard speckle image in the depth interval is calculated. For example, the depth value of the speckle image to be compared is 18 cm, and a standard speckle image with a depth interval of 10-20 cm can be selected in the standard speckle image set for comparison, and the similarity between the two images can be calculated. In the embodiment, depth information is introduced into the speckle images, a standard speckle image set containing standard speckle images in different depth ranges is established, and the standard speckle images are compared with the standard speckle images in corresponding depths, so that the algorithm has stronger robustness and higher safety.
The standard speckle image of this embodiment has depth values, and a certain standard speckle image can represent the standard speckle image of a certain depth interval according to actual conditions, and the standard speckle image is obtained by first projecting laser onto different faces at different angles and different distances with a laser projector under different scenes, then acquiring the face images with an infrared camera, and performing speckle extraction on face areas of the face images to obtain speckle images of the face areas, acquiring depth values of the speckle images with a depth camera, dividing the speckle images with different depth values into corresponding depth intervals according to the depth values, for example, dividing a 0-100 cm depth value into several segments, such as 5 segments, 1-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm, when the depth value of a certain speckle image is 18 cm, and then classifying the speckle images into subsets of 1-20 cm depth intervals, classifying the speckle images into the subsets of 41-60 cm depth intervals when the depth value of one speckle image is 58 cm, repeating the steps, grouping all the speckle images into the subsets of the corresponding depth intervals, calculating the average image of all the speckle images in each depth interval subset, namely the standard speckle image of the depth interval subset, and storing the average image into the standard speckle image set. When the speckles are compared, the standard speckle images corresponding to the depth intervals can be compared so as to adapt to the speckles with different distances.
Various other changes and modifications to the above-described embodiments and concepts will become apparent to those skilled in the art from the above description, and all such changes and modifications are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (8)
1. A face recognition living body judgment method is characterized by comprising the following steps:
s1, the laser projector projects laser onto the face to be recognized, and the laser forms speckles on the face to be recognized;
s2, the infrared camera acquires the speckle images of the face to be recognized and sends the speckle images of the face to be recognized to the recognition and judgment unit;
s3, after receiving the speckle images sent by the infrared camera, the identification and judgment unit identifies the face area in the speckle images;
s4, extracting images of all speckles in the face area;
and S5, comparing all the extracted speckle images with the standard speckle images one by one, calculating the similarity between each speckle image and the standard speckle images, then calculating the mean value of the similarity of all the speckle images, taking the mean value of the similarity as a living body recognition score, and if the living body recognition score is larger than a set threshold value, considering that the face to be recognized is a living body face.
2. The face recognition living body judgment method according to claim 1, characterized in that: in step S4, when an image of a certain speckle is extracted from the face region, the sub-pixel coordinates of the speckle center are determined, and an image of a region of a predetermined size centered on the speckle center is extracted.
3. The face recognition living body judgment method according to claim 1, characterized in that: and representing the similarity of a certain speckle image and a standard speckle image by using Euclidean distance.
4. The face recognition living body judgment method according to claim 3, characterized in that: the similarity of a certain speckle image and a standard speckle image is equal to the negative value of the Euclidean distance between the speckle image and the standard speckle image.
5. The face recognition living body judgment method according to claim 1, characterized in that: the standard speckle images are obtained by projecting laser onto different faces at different angles and different distances by using a laser projector under different scenes, respectively performing speckle extraction on face areas of the face images after the face images are acquired by an infrared camera, and calculating an average image of all the extracted speckle images, namely the standard speckle image.
6. The face recognition living body judgment method according to claim 1, characterized in that: after step S4, the depth of all speckle images is obtained by the depth camera, and in step S5, all the speckle images with depth information are compared with the standard speckle images corresponding to the depth of the standard speckle image set one by one, the similarity between the speckle images and the corresponding standard speckle images is calculated, and then the mean value of the similarity between all the speckle images is calculated as the living body identification score.
7. The face recognition living body judgment method according to claim 6, characterized in that: the standard speckle image set comprises a plurality of standard speckle images, each standard speckle image represents a certain depth interval, and when similarity comparison is carried out, the standard speckle images corresponding to the depth intervals are selected from the standard speckle image set for comparison according to the depth values of the speckle images, and the similarity between the speckle images and the standard speckle images of the depth intervals is calculated.
8. The face recognition live body judgment method according to claim 6 or 7, characterized in that: the standard speckle images in the standard speckle image set are acquired according to the following method: the method comprises the steps of projecting laser to different faces at different angles and different distances by using a laser projector under different scenes, obtaining face images by using an infrared camera, performing speckle extraction on face areas of the face images respectively to obtain images of all speckles in the face areas, obtaining depth values of the speckle images by using a depth camera, dividing the speckle images with different depth values into corresponding depth intervals according to the depth values, calculating an average image of all the speckle images in each depth interval subset, namely a standard speckle image of the depth interval subset, and storing the average image into a standard speckle image set.
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