A Normalized Light CNN for Face Recognition

Abstract

In recent years, achievements of deep learning in face recognition have exceeded those of traditional machine learning methods do. To achieve better recognition performance, the architectures of the neural network tend to be deeper and more complex, which wastes the time and space. Aiming at this issue, a series of light convolutional neural network (Light CNN) has been proposed, which consume less hardware resources while has excellent performance. In this paper, we propose a Normalized Light CNN model modified from a Light CNN model. We introduced a normalized layer to this model in both training and test phase. The normalized layer normalizes the output features, making it represent images better. We evaluate our model on LFW dataset. The accuracy of face verification reaches 98.46%, which is better than the original model.