[1]FANG Tao,CHEN Zhiguo,FU Yi.Face recognition method based on neural network multi-layer feature information fusion[J].CAAI Transactions on Intelligent Systems,2021,16(2):279-285.[doi:10.11992/tis.201907053]
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Face recognition method based on neural network multi-layer feature information fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 279-285 Column: 学术论文—机器感知与模式识别 Public date: 2021-03-05
Title:
Face recognition method based on neural network multi-layer feature information fusion
Author(s):
FANG Tao1;  CHEN Zhiguo1;  FU Yi1; 2
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Wuxi Research Center of Environmental Science and Engineering, Wuxi 214153, China
Keywords:
face recognition; facial feature; neural network; information fusion; feature fusion; decision fusion; feature extraction; similarity fusion
CLC:
TP391.41
DOI:
10.11992/tis.201907053
Abstract:
Because the structure of the face is complex and the structural features of different faces are similar, it is difficult to extract facial features that are suitable for classification. Neural networks generate good results, and the recent improvements in many loss functions can help extract the required features. However, a single depth feature does not make full use of the complementarity of multi-layer features. To solve these problems, we propose a face recognition method based on the fusion of neural-network multi-layer feature information. First, we select the ResNet network structure to improve the outcome, then we extract the multi-layer features in the neural network. These features are then mapped onto the sub-spaces. Next, adaptive weighted fusion is performed of the defined central variables in the respective sub-spaces. To realize further improvement, all the features are sent to the Softmax classifier, and the classification results are fused in the same way by adaptive weighted decision-making. The training network learns the appropriate central variable, which is applied to calculate the weighted fusion similarity. Under the same conditions, based on the AM-Softmax loss function, the recognition of the fusion feature on the Labeled Faces in the Wild database increased by 1.6%, and the fusion similarity increased by 2.2%. We conclude that the proposed method effectively improves the face recognition rate and extracts more suitable facial features.
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