Aiming at the problem of spoofing attack in face information authentication system, this thesis proposes a face detection algorithm by using information loss characteristic in color space transformation. Gabor filter banks are used to enhance the texture features of face images in multi-scale and multi-direction. Gauss Radial Basis Function Classifier classifies the feature descriptors extracted by SURF operator, and distinguishes face living objects and the spoofing attacks of illegal users. The inter-class variance, intra-class variance and separability criterion J of the original image features and texture enhanced features are calculated by using the inter-class variance to measure the separability before and after the feature improvement. Finally, the algorithm is tested in public data dataset Replay-Attack and CASIA-FASD. After the enhancement of the color texture image, the key features of the face are enhanced and the general features are suppressed. The variance between the background and the target is increased, and the intra-class variance is reduced. Feature separability is enhanced to be more robust,allowing texture details to be effectively utilized.. The experimental results show that the proposed algorithm can effectively judge the human face and spoofing attacks in real time.