Abstract: With the wide range application of Authentication technology，All kinds of spoofing attacks occur when a person tries to masquerade system by exhibiting fake faces of an authorized client. Hence, We propose to approach the problem of face liveness detection based on gray level co-occurrence matrix(GLCM) and wavelet analysis. Our method focus on analyzing the facial image texture difference between a live person and a face print. We extract the four features -energy, entropy, moment of inertia and the correlation on the basis of GLCM .In addition ,It obtain the high frequency subbands coefficients using secondary decomposition of wavelet transform for classification recognition. Primary experiments on the publicly available NUAA photo-imposter database show that the algorithm reduce the computational complexity and improve the detection accuracy.