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### 加权特征融合的密集连接网络人脸识别算法

1. 哈尔滨理工大学 计算机科学与技术学院，哈尔滨 150080
• 出版日期:2019-07-01 发布日期:2019-07-08

### Densely Connected Convolutional Networks Face Recognition Algorithm Based on Weighted Feature Fusion

WANG Xiaoyu+, HAN Changlin, HU Xinhao

1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
• Online:2019-07-01 Published:2019-07-08

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.