基于深度学习的有遮挡人脸识别方法研究

doi:10.16180/j.cnki.issn1007-7820.2022.01.006

Abstract

Abstract:

In view of the large amount of calculation in traditional CNN in occluded face recognition, this study proposes a new PCANet deep learning network for face recognition based on L1-2DPCA. The proposed network uses L1-2DPCA to learn filters of multiple convolutional layers. After the convolutional layer, pooling is performed through binary hashing and block-by-block histogram. CNN, PCANet, 2DPCANet and L1-PCANet are compared, and the proposed network is tested on AR and RMFD face data sets. The results show that in all tests, the recognition performance of the deep learning network proposed in this study is better than other networks. Due to the use of L1 norm, the proposed deep learning network has strong robustness to the changes of outliers and training images.

Key words: face recognition, occlusion, deep learning, L1-2DPCA, two-dimensional principal component analysis, L1 norm, CNN, robustness

CLC Number:

TN432

CHENG Xiaoya,ZHANG Lei. Research on Occluded Face Recognition Method Based on Deep Learning[J].Electronic Science and Technology, 2022, 35(1): 35-39.

Figures/Tables 8

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References 15

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	无遮挡	戴墨镜	戴围巾	戴墨镜和围巾
CNN	78.94±4.05	78.84±3.95	79.34±3.85	80.01±3.85
PCANet	78.72±3.15	78.80±4.51	79.45±3.97	80.40±4.10
2DPCANet	83.21±4.21	83.79±4.35	83.81±3.02	83.44±4.27
L1-PCANet	86.92±0.51	86.79±0.42	87.15±0.35	86.46±0.22
本文方法	89.35±0.39	88.78±0.32	88.90±0.45	88.52±0.19

	10%	20%	30%	50%
PCANet	92.59±0.39	87.97±0.42	75.02±0.46	45.23±0.75
2DPCANet	93.97±0.31	88.69±0.49	78.85±0.85	56.41±0.68
L1-PCANet	94.26±0.41	91.35±0.49	84.26±0.59	66.11±0.63
本文方法	98.88±0.16	98.12±0.17	95.53±0.22	83.21±0.72

	1	2	3	4	5	6	7	均值	RMSE
PCANet	76.56	77.56	76.90	68.73	74.40	70.56	79.84	74.94	4.42
2DPCANet	73.81	76.81	76.81	74.31	78.97	74.14	74.64	75.64	2.42
L1-PCANet	83.49	82.83	82.66	83.16	85.66	83.16	82.49	83.35	1.65
本文方法	86.64	85.47	86.14	87.14	87.97	87.47	87.47	86.90	1.45

Research on Occluded Face Recognition Method Based on Deep Learning

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