Anti-cheat detection method for human face in identity authentication system

Abstract

The invention relates to an anti-cheat detection method for a human face in an identity authentication system, which comprises the steps of firstly, extracting spatial information of pixels by using a local binary pattern, grayscale distribution statistics and grayscale co-occurrence matrix to obtain texture features of a space domain; secondly, extracting a low frequency complex coefficient and a high frequency complex coefficient by using two-dimensional dual-tree complex wavelet decomposition to obtain texture feature of a frequency domain; then performing feature fusion by using PCA dimension reduction so as to fuse the texture features of the space domain and the texture features of the frequency domain; and finally, performing feature fusion on the texture features of the space domain and the texture features of the frequency domain, and detecting and judging a real/fake human face image by using an SVM classifier. According to the invention, the texture features of the space domain and the texture features of the frequency domain are fused, especially the texture features are extracted by using the time shift invariance and the direction selectivity of two-dimensional dual-tree complex wavelet decomposition in the frequency domain, and dimension reduction and decorrelation are performed on the fused features by using PCA, so that the calculation complexity is low, the redundancy is low, the consumption of time and space is saved, the accuracy of human face cheat detection is improved, and the security of human face cheating in the identity authentication system is enhanced.

Description

technical field [0001] The invention relates to the field of face anti-spoofing detection in an identity authentication system, in particular to a Background technique [0002] As a convenient user authentication technology, automatic face recognition has received increasing attention in various access control applications, especially mobile phone unlocking. With the release of the face unlock function in the Android mobile operating system, facial recognition has become another biometric technology for mobile phones, similar to fingerprint authentication (Touch ID) in the IOS system. Unlike fingerprint authentication, facial recognition does not require any additional sensors, as all smartphones are equipped with front-facing cameras. However, similar to other biometric modalities, we need to address concerns about face spoofing attacks on face recognition systems, especially in unconstrained perception and uncooperative subject scenarios. In order to verify the authentic...

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Problems solved by technology

These methods are easily affected by lighting conditions and have high computational complexity. They ca

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