基于多特征融合的人脸识别算法

doi:10.16180/j.cnki.issn1007-7820.2019.07.009

Abstract

Abstract:

The texture information extracted by the LBP descriptor and the edge feature extracted by the GMQ operator cannot effectively and comprehensively describe the facial feature. To solve the problems, a novel multiple feature fusion algorithm based on Markov Stationary Features model was proposed. Firstly, the edge features obtained by GMQ operator as well as the texture features by LBP descriptor were fused with the MSF model respectively. Then the two MSF-based features were fused by linear weighting. Finally, experiments on the ORL dataset showed that the proposed algorithm could achieve an accuracy of 95.83%. Compared with a single feature extraction algorithm and a common face recognition algorithm, the effectiveness of the proposed method was proved.

Key words: face recognition, local binary pattern, gradient magnitude quantization, markov stationary features, linear weighting fusion, ORL database

CLC Number:

TP391.41

SU Rao,LI Feifei,CHEN Qiu. Face Recognition Algorithm Based on Multiple Feature Fusion[J].Electronic Science and Technology, 2019, 32(7): 43-48.

Figures/Tables 10

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References 22

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滤波器模板尺寸	识别率
无	79.17%
3×3	86.67%
5×5	90.00%
7×7	89.17%
9×9	85.00%
11×11	6.67%
13×13	84.17%
15×15	84.17%
17×17	83.30%

等价模式	识别率
无	75.00%
58种	86.67%

ω ₁	识别率
1	90.00%
1/10	90.00%
2/10	91.67%
3/10	95.83%
4/10	95.83%
5/10	94.17%
6/10	93.30%
7/10	93.30%
8/10	93.30%
9/10	90.00%
0	86.67%

算法	识别率
LBP	84.10%
ICA	85.00%
PCA	85.30%
HOG	90.24%
MSF-LBP	86.67%
MSF-GMQ	90.00%
GMQ-MSF-LBP	95.83%

Face Recognition Algorithm Based on Multiple Feature Fusion

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