|Table of Contents|

Multi-stage Feature Fusion PCANet and Its Application to Face Recognition(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年02期
Page:
127-133
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Multi-stage Feature Fusion PCANet and Its Application to Face Recognition
Author(s):
Chen Feiyue1; Zhu Yulian2; Chen Xiaohong3
(1.College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)(2.Fundamental Experimental Teaching Department,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)(3.College of Science,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
Keywords:
face recognition; PCANet; DenseNet; feature fusion; PCANet_dense
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2021.02.018
Abstract:
Principal component analysis network(PCANet)is a simple deep learning network model but shows strong application potential in many fields,especially in image recognition. In this paper,a new multi-stage feature fusion model based PCANet(PCANet_dense)is constructed by analyzing the structure of PCANet. Different from PCANet which only takes the output of the former layer as the input of the latter layer,PCANet_dense uses the feature information of different layers. In the two-layer network structure,it firstly cascades the original image features with the output of the first layer network,and then takes the fused features as the input of the second layer network. In the three-layer network structure,it cascades the output of the first and the second layer in series as the input of the third layer network. Due to the use of more information in each layer(except the first layer)in training process,the new model gets good performance. In order to verify the effectiveness of the proposed method,several comparative experiments on different face datasets,such as ORL,AR and Extended Yale B,are established. The network model of PCANet and PCANet_dense are well constructed on CMU PIE dataset before all the experiments. The experimental results show that the proposed multi-stage feature fusion PCANet method achieves better performance than PCANet.

References:

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Last Update: 2021-06-30