CN112132046A - Static living body detection method and system - Google Patents
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Abstract
The invention discloses a static living body detection method and a system, wherein the method comprises the following steps: collecting a visible light image and an infrared image of a user; carrying out face detection on both the visible light image and the infrared image, if a face region is detected, extracting the visible light face region image from the visible light image, and extracting the infrared face region image from the infrared image; and performing living body detection on the visible light face area image and the infrared face area image by adopting a deep neural network to obtain a living body detection result. The method improves user experience.
Description
Technical Field
The invention relates to the technical field of face recognition, in particular to a static living body detection method and a static living body detection system.
Background
At present, along with the improvement of the recognition rate, the face recognition system is in commercial use in the aspects of railway passenger transport, bank transaction, mobile phone unlocking, face brushing payment and the like, and gradually permeates the fields of schools, buildings, security protection and the like. The live body detection is a technology for judging whether a captured face is a real face or a forged face attack. If the face recognition function is to recognize a person, the live body detection function is to recognize. In order to prevent cheating of photos, videos, masks and the like, the living body detection is generally embedded in a module in face detection and face recognition or verification to verify whether the living body detection is the user, so that real person identity verification is completed for high-security scenes such as finance, entrance guard and the like, and a face recognition system can operate safely and stably.
Face detection and recognition are extremely interesting and challenging areas in biometric-based identity authentication. The face detection and recognition technology has a high development prospect and economic benefit in the fields of public security investigation, access control systems, target tracking, other civil safety control systems and the like. However, although the conventional face recognition system can recognize different faces, it is difficult to determine whether the face is a living body, a photograph or a mask. Compared with biological characteristics such as fingerprints and irises, the human face characteristics are most easily acquired.
However, with face recognition technology alone, the face biometric cannot be used as a security key, because the face biometric is particularly easy to be collected and used for attack. In various occasions such as airport security check, work attendance, company entrance guard, bank account opening, online payment and the like, if a face recognition system is attacked, great loss is caused to individuals and the society. The biometric detection technology is to eliminate the potential safety hazard in the face recognition technology, and in the biometric feature recognition system, in order to prevent a malicious person from forging and stealing the biometric features of other people for identity authentication, the biometric feature recognition system needs to have a biometric detection function. The living body detection comprises monocular silence detection and binocular silence detection, monocular needs to be matched with user actions, but human face living body detection needing to be matched with the user has long interaction time and poor user experience.
Disclosure of Invention
The invention aims to provide a static living body detection method and a static living body detection system so as to improve user experience.
In order to solve the above technical problems, the present invention provides a static biopsy method, comprising:
collecting a visible light image and an infrared image of a user;
carrying out face detection on both the visible light image and the infrared image, if a face region is detected, extracting the visible light face region image from the visible light image, and extracting the infrared face region image from the infrared image;
and performing living body detection on the visible light face area image and the infrared face area image by adopting a deep neural network to obtain a living body detection result.
Preferably, a face detection algorithm is adopted to perform face detection on both the visible light image and the infrared image.
Preferably, the extracting the visible light face region image from the visible light image includes:
and carrying out face quality evaluation, face key point detection and face alignment on the visible light image to obtain a visible light face area image.
Preferably, the extracting an infrared face area image from an infrared image includes:
and performing face quality evaluation, face key point detection and face alignment on the infrared image to obtain an infrared face area image.
The invention also provides a static living body detection system for realizing the method, which comprises the following steps:
the acquisition module is used for acquiring visible light images and infrared images of a user;
the extraction module is used for carrying out face detection on both the visible light image and the infrared image, extracting the visible light face region image from the visible light image and extracting the infrared face region image from the infrared image if the face regions are detected;
and the detection module is used for performing living body detection on the visible light face area image and the infrared face area image by adopting a deep neural network to obtain a living body detection result.
Preferably, the extraction module is specifically configured to perform face detection on both the visible light image and the infrared image by using a face detection algorithm.
Preferably, the extraction module comprises:
the detection unit is used for carrying out face detection on both the visible light image and the infrared image;
and the extraction unit is used for extracting a visible light face region image from the visible light image and extracting an infrared face region image from the infrared image when the face region is detected.
Preferably, the extraction unit includes:
the first evaluation subunit is used for carrying out face quality evaluation, face key point detection and face alignment on the visible light image to obtain a visible light face area image;
and the second evaluation subunit is used for carrying out face quality evaluation, face key point detection and face alignment on the infrared image to obtain an infrared face area image.
The invention provides a static living body detection method and a system, which are used for collecting visible light images and infrared images of a user; carrying out face detection on both the visible light image and the infrared image, if a face region is detected, extracting the visible light face region image from the visible light image, and extracting the infrared face region image from the infrared image; and performing living body detection on the visible light face area image and the infrared face area image by adopting a deep neural network to obtain a living body detection result. Therefore, the visible light image and the infrared image are collected, the face detection is carried out by utilizing the visible light image and the infrared image, the cooperation of a user is not needed, the user experience is improved, the real-time non-inductive entrance guard detection can be realized, and the external influence of illumination and the like is small.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a static biopsy method according to the present invention;
FIG. 2 is a flow chart of an implementation of in vivo detection;
FIG. 3 is a schematic structural diagram of a static in-vivo detection system according to the present invention.
Detailed Description
The core of the invention is to provide a static living body detection method and a static living body detection system so as to improve user experience.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a static biopsy method according to the present invention, which includes the following steps:
s11: collecting a visible light image and an infrared image of a user;
s12: carrying out face detection on both the visible light image and the infrared image, if a face region is detected, extracting the visible light face region image from the visible light image, and extracting the infrared face region image from the infrared image;
s13: and performing living body detection on the visible light face area image and the infrared face area image by adopting a deep neural network to obtain a living body detection result.
Therefore, the method not only collects the visible light image, but also collects the infrared image, and utilizes the visible light image and the infrared image to detect the face of the person, so that the cooperation of the user is not needed, and the user experience is improved. In addition, the system can realize real-time non-inductive access control detection and is little influenced by the outside such as illumination.
Based on the above method, further, in step S12, a face detection algorithm is used to perform face detection on both the visible light image and the infrared image.
Further, in step S12, the process of extracting the visible light face region image from the visible light image specifically includes: and carrying out face quality evaluation, face key point detection and face alignment on the visible light image to obtain a visible light face area image.
Further, in step S12, the process of extracting the infrared face region image from the infrared image specifically includes: and performing face quality evaluation, face key point detection and face alignment on the infrared image to obtain an infrared face area image.
Referring to fig. 2, fig. 2 is a flowchart of an implementation of in vivo detection, specifically, the implementation flow is as follows:
step 1, video input and face detection;
the living body detection is carried out by combining the infrared input image, so that a better effect can be obtained, and the living body detection device can be more suitable for the change of the external environment. When the light source is used in scenes such as residential area access control, visible light and infrared light supplementing light sources need to be equipped. And respectively carrying out face detection in the visible image and the infrared image, and carrying out the next processing only when a face area is detected simultaneously. The face detection algorithm adopts an SSD-like structure, integrates an FPN module, sets an anchor according to the size and distribution characteristics of a detection target, and combines a lightweight backbone network to perform face detection. And then, the positions of key points of the human face, namely the centers of the left and right eyes, the left and right mouth corners and the nose tip, are positioned on the basis of human face detection. And judging whether the detected input face is a face picture with qualified quality or not by a face quality evaluation mode, and if the detected input face is the face picture with qualified quality, intercepting a face area on the image, expanding and carrying out affine transformation to obtain a front picture corresponding to the face.
Step 2, detecting key points and aligning faces;
the affine transformation is a kind of spatial rectangular coordinate transformation, which is a linear transformation from two-dimensional coordinates to two-dimensional coordinates. Affine transformations can be achieved by the composition of a series of atomic transformations, including translation, scaling, flipping, and rotation. In the method, 5 feature points are regressed in face detection, and the detected face is corrected relative to a standard input face. In the correction process, in order to keep the sizes of the input face pictures consistent as much as possible, the face image corrected relative to the standard face is zoomed, and the zooming proportion is that the eye distance of the actually detected human is divided by the eye distance of the standard face. And sending the corrected face image into a living body detection network for judgment.
And 3, feature extraction and logic output.
And respectively carrying out face detection in the visible light and the infrared picture, simultaneously carrying out face quality evaluation and face alignment on the detected face region, and sending the aligned visible light face region and infrared face region into a deep neural network to judge whether the face region is a living body.
The output of the final result can be judged by adopting a method of detecting continuous multi-frames as living bodies.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a static biopsy system according to the present invention, the system is used for implementing the above method, and the system includes:
the acquisition module 101 is used for acquiring visible light images and infrared images of a user;
the extraction module 102 is configured to perform face detection on both the visible light image and the infrared image, extract a visible light face region image from the visible light image if a face region is detected, and extract an infrared face region image from the infrared image;
the detection module 103 is configured to perform living body detection on the visible light face area image and the infrared face area image by using a deep neural network, so as to obtain a living body detection result.
Therefore, the system not only collects the visible light images, but also collects the infrared images, and utilizes the visible light images and the infrared images to detect the human face, so that the cooperation of users is not needed, and the user experience is improved. In addition, the system can realize real-time non-inductive access control detection and is little influenced by the outside such as illumination.
Based on the system, further, the extraction module is specifically configured to perform face detection on both the visible light image and the infrared image by using a face detection algorithm.
Further, the extraction module comprises:
the detection unit is used for carrying out face detection on both the visible light image and the infrared image;
and the extraction unit is used for extracting a visible light face region image from the visible light image and extracting an infrared face region image from the infrared image when the face region is detected.
Further, the extraction unit includes:
the first evaluation subunit is used for carrying out face quality evaluation, face key point detection and face alignment on the visible light image to obtain a visible light face area image;
and the second evaluation subunit is used for carrying out face quality evaluation, face key point detection and face alignment on the infrared image to obtain an infrared face area image.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above description describes a static biopsy method and system provided by the present invention in detail. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (8)
1. A static liveness detection method, comprising:
collecting a visible light image and an infrared image of a user;
carrying out face detection on both the visible light image and the infrared image, if a face region is detected, extracting the visible light face region image from the visible light image, and extracting the infrared face region image from the infrared image;
and performing living body detection on the visible light face area image and the infrared face area image by adopting a deep neural network to obtain a living body detection result.
2. The method of claim 1, wherein the face detection algorithm is used to perform face detection on both the visible light image and the infrared image.
3. The method of claim 1, wherein extracting the visible light face region image from the visible light image comprises:
and carrying out face quality evaluation, face key point detection and face alignment on the visible light image to obtain a visible light face area image.
4. The method of claim 1, wherein the extracting the infrared face region image from the infrared image comprises:
and performing face quality evaluation, face key point detection and face alignment on the infrared image to obtain an infrared face area image.
5. A static liveness detection system for implementing the method according to any one of claims 1 to 4, comprising:
the acquisition module is used for acquiring visible light images and infrared images of a user;
the extraction module is used for carrying out face detection on both the visible light image and the infrared image, extracting the visible light face region image from the visible light image and extracting the infrared face region image from the infrared image if the face regions are detected;
and the detection module is used for performing living body detection on the visible light face area image and the infrared face area image by adopting a deep neural network to obtain a living body detection result.
6. The system of claim 5, wherein the extraction module is specifically configured to perform face detection on both the visible light image and the infrared image using a face detection algorithm.
7. The system of claim 5, wherein the extraction module comprises:
the detection unit is used for carrying out face detection on both the visible light image and the infrared image;
and the extraction unit is used for extracting a visible light face region image from the visible light image and extracting an infrared face region image from the infrared image when the face region is detected.
8. The system of claim 7, wherein the extraction unit comprises:
the first evaluation subunit is used for carrying out face quality evaluation, face key point detection and face alignment on the visible light image to obtain a visible light face area image;
and the second evaluation subunit is used for carrying out face quality evaluation, face key point detection and face alignment on the infrared image to obtain an infrared face area image.
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| CN113989903A (en) * | 2021-11-15 | 2022-01-28 | 北京百度网讯科技有限公司 | Face living body detection method and device, electronic equipment and storage medium |
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Application publication date: 20201225 |