Abstract: Aiming at the problems of low non-constrained feature discriminative ability and poor face recognition performance of current many algorithms, an improved face recognition algorithm based on deep learning is proposed. By training multi-task cascading convolutional neural networks, face detection and face normalization for unconstrained training face images are accomplished, which improves the face information of the training image and reduces the interference to the model. At the same time, the model is jointly supervised and trained by using Softmax Loss and Central Loss to compact intra-class and to disperse inter-class. The experimental results show that the algorithm improves the feature discriminant ability of the model and achieves higher recognition rate on the LFW standard test set.

Key words: deep learning, convolutional neural network, face detection, face recognition