关键词: 人脸识别, 加权密集连接, 加权特征融合, 多损失函数

Abstract: In unconstraint condition, face recognition is usually affected by expression change, angle deviation, occlusion and exposure in different degrees, also other comprehensive factors. At the same time, interfering factors exist in deep convolutional neural networks, such as lots of parameters, gradient diffusion or disappearance during training. To solve mentioned concerns, this paper proposes a FuseNet network model, which effectively utilizes local feature information such as eyes, nose and mouth as well as global feature information like facial contour, and puts forward the multi-loss function to reduce the gap between the class features and increase further distance between class characteristics, which effectively enhances the robustness of face recognition under constraint conditions. The weighted, densely connected convolutional neural network, is adapted to extract the global features of faces in which densely connected module effectively solves the problem like parameter redundancy and gradient diffusion caused by the deep network. Additionally, proposed different connection weights enable the network to make full use of selected characteristics. The experimental results show that the proposed FuseNet network achieves better recognition rate and robustness, not only in the closed CASIA-WebFace dataset, but also in open FLW dataset and MegaFace dataset.

Key words: face recognition, weighted densely connected, weighted feature fusion, multi-loss function