Abstract
Face recognition via deep learning has achieved a series of breakthrough in recent years. The deeply learned features are required to be discriminative and generalized enough for identifying new unseen classes without label prediction. Therefore, this paper improved the performance of deep learning by aligning the training data and enhancing the preprocessing. Moreover, the designed model, named Lightened Convolutional Neural Network model, and the center loss layer jointly to enhance the discriminative of the designed network features. The network is trained on the self-expanding CASIA-WebFace database and tested on the Labeled Faces in the Wild (LFW) database. Experimental results show that the proposed network model brings significant improvement in the accuracy of face recognition, compared with the original CNN model.
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