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.