Compressed Deep Convolution Neural Network for Face Recognition
Deep convolution neural network (CNN) has achieved a great success on face recognition techniques. But most of CNN models tend to be much deeper, which are at the expenses of high consumption of computation and storage. So, it is hard for these deep CNNs applied to mobile equipments because of poor computational and memory resources. To alleviate this issue, this paper optimizes a lightened baseline CNN model by adopting an additional contrastive loss to learn more discriminative features. To further reduce the number of parameters, a pruning strategy is tried to compress our model, which slightly improves accuracy on the LFW dataset with the compression ratio of 0.7. Finally, experimental result shows that the proposed method achieve state of the art results with much smaller size and fewer training data. Introduction In recent years, face recognition in unconstrained conditions achieves a significant breakthrough. Among the traditional algorithms of face recognition, the first step is to extract features from raw face images, such as SIFT, HOG, LBP features with high dimensions. Then use a classifier to complete our recognition task. While CNN network could learn feature vectors to represent a face image automatically, it seems to be more helpful to use CNN model for face recognition task. Deep CNN achieves high accuracy at the expenses of high consumption of computation and storage with large number of parameters. Although NVIDIA company has developed a series of high performance computing GPU to make deep learning more hopeful, it is less practical for mobile equipment because of their poor computation and storage resource. So it is important to design a CNN model with fast speed, small storage as well as high accuracy. To achieve this goal, this work utilizes the lightened CNN model and optimize it by adding a contrastive loss to minimize the intra class distances of deep features. Finally we use the pruning method to convert the dense model to a sparse layer to reduce the size of model storage. The rest of our paper is organized as follows: Section 2 compares our work against recent works on face recognition. The details of our proposed method and experiment are presented in Section 3 and Section 4 respectively. Finally, we draw a conclusion in Section 5. Related work Many popular face verification methods design a deep CNN network and extract features as the representation of a face. DeepFace [1] trains a deep CNN model with a large face database which contains 4M face images of more than 4K subjects as training data. DeepFace also uses a 3D alignment as data preprocessing to handle out of plane rotations. DeepID [2,3,4] series could be regarded as a set of representative work of face recognition with deep learning. DeepID [2] was firstly proposed by Sun et al. trained 25 CNN models with four convolution layers. To further improve accuracy, DeepID2 [3] combined identification and verification supervisory signals based on DeepID, while DeepID2+ [4] added verification supervisory signal for each layer. Google proposed FaceNet [5] in CVPR 2015, which is a very deep CNN model containing 22 layers and trained on totally about 200M face images with 8M identities and adopted triplet loss as supervisory signal. 4th International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2016) Copyright (c) 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY NC license (http:/creativecommons.org/licenses/by nc/4.0/ Advances in Computer Science Research, volume 71