Research on Fast Face Recognition Method Based on Double Decision Subspace

Because a large number of labeled face data samples in special scenes need a large number of training samples with identity markers, and it is impossible to accurately extract the characteristics of small samples, a fast face recognition method based on double decision subspace is proposed. A feature recognition structure based on double decision subspace is constructed to preprocess the face image and separate the local features of several corresponding face images. The local binary pattern is used to extract the local texture features of the face, and the deep convolution network face fast recognition model is constructed. The convolution network is used to share the weight, pool, and downsampling to reduce the complexity of the model. The constructed recognition model is used to recognize the features of the face image, and the fast face recognition is effectively completed. The experimental results show that the designed method has high recognition accuracy, less average recognition time, and good recognition performance.