An Effective Face Verification Algorithm to Fuse Complete Features in Convolutional Neural Network.

Face verification for on line application is a difficult problem and many researchers have tried to solve it by convolutional neural network. Among of them, most works used the last-hidden layer as the feature of face, and abandoned the features in the lower layers which indicate local information. To remedy this, we extract features of all layers in the convolutional neural network, and fuse these features together after dimensionality reduction with PCA. Then these features are utilized for face verification with neural network classifier. Experiment results show that complete features can improve the verification rate effectively than using the last-hidden layer only.