Abstract
For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.
Highlights
With the application of face recognition technology in the identification scene such as access security check and face payment, the methods of attack and fraud against face recognition system appear
For face detection scenes under complex lighting, we propose an improved image preprocessing algorithm combined with local contrast in the face area, which effectively improves the performance of the face detection algorithm
Traditional detection techniques can be divided into three categories: the face detection based on feature, the face detection based on template, and the face detection based on statistics. is paper uses face front detection API provided by Dlib, which uses gradient direction histogram feature to achieve face detection. e face detection algorithm based on gradient direction histogram can maintain good immutability of image texture and optical deformation and ignore the slight texture and changes in expression
Summary
With the application of face recognition technology in the identification scene such as access security check and face payment, the methods of attack and fraud against face recognition system appear. Some researchers use infrared camera, depth camera, and other sensors to collect different modes of face images to achieve living detection [1,2,3]. We will study the monocular static and silent living detection and achieve the living detection task by analyzing the difference between real face and fake face in image texture, facial structure, action change, and so on. Remote photoplethysmography (rPPG) is another effective noncontact living signal extraction method, which provides a basis for face living detection by observing face images to calculate the changes in blood flow and flow rate [6, 7], but the rPPG method has strict requirements for algorithm application environment. Is work proposed a network that fuses dynamic and texture information to represent face and detect the attacks. For face detection scenes under complex lighting, we propose an improved image preprocessing algorithm combined with local contrast in the face area, which effectively improves the performance of the face detection algorithm
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