基于深度学习与特征融合的人脸识别算法

doi:10.16180/j.cnki.issn1007-7820.2020.04.004

Abstract

Abstract:

The convolutional neural network performed well on face recognition, but the extracted facial feature ignores the local structure features of the face. In order to address this problem, a novel method was proposed, which was based on deep learning and feature fusion. This algorithm made the extracted facial features more comprehensive by using a combination of local binary pattern information and original image information as the input of SDFVGG network, which was a neuralnetwork fusing shallow and deep features of VGG network. Experimental results on the CAS-PEAL-R1 face database demonstrated that the proposed algorithm was very effective for improving the accuracy of face recognition, and achieved a maximum face recognition rate of 98.58% which was better than traditional algorithms and general convolutional neural networks.

Key words: feature extraction, feature fusion, convolutional neural network, SDFVGG, local binary pattern, face recognition

CLC Number:

TP391

SI Qin,LI Feifei,CHEN Qiu. Face Recognition Algorithm Based on Deep Learning and Feature Fusion[J].Electronic Science and Technology, 2020, 33(4): 18-22.

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References 16

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网络	识别率/%
网络	PE	PA	PL
VGG	99.69	97.66	98.44
SDFVGG	99.69	98.18	99.22

网络输入	识别率/%
网络输入	PE	PA	PL
原图	99.69	98.18	99.22
LBP	99.06	96.61	97.66
原图+LBP	98.58	97.12	98.05

不同方法	识别率/%
不同方法	PE	PA	PL
GPCA+LDA^[13] LGBPHS^[14] PZM-RBFNN^[15] HGPP^[14] LLGP^[13] FHOGC^[16] Ours	90.60 95.20 84.80 96.40 98.00 94.90 98.58	82.80 86.80 93.40 91.90 92.00 90.30 97.12	44.80 51.00 63.40 61.70 55.00 68.70 98.05

Face Recognition Algorithm Based on Deep Learning and Feature Fusion

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