2D/3D FACE RECOGNITION USING NEURAL NETWORK BASED ON HYBRID TAGUCHI-PARTICLE SWARM OPTIMIZATION

被引：0

作者：

Lin, Cheng-Jian ^{[1
]}

Wang, Jyun-Guo ^{[2
]}

Chen, Shyi-Ming ^{[2
]}

机构：

[1] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung Cty 411, Taiwan

[2] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2011年 / 7卷 / 02期

关键词：

Face recognition (FR); Taguchi method; Particle swarm optimization (PSO); Principal component analysis (PCA); Multilayer neural networks (MLNN); Gabor wavelet;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a neural network classifier with hybrid evolutionary algorithm for solving 2D/3D face recognition problems. We first use Gabor wavelets to extract local features at different scales and orientations for gray facial images, then combine the texture with the surface feature vectors based on principal component analysis (PCA) to obtain feature vectors. We propose a neural network classifier based on hybrid Taguchi-particle swarm optimization (HTPSO) algorithm for face recognition. Experimental results demonstrate that the proposed HTPSO learning method has a better recognition rate than those of other approaches.

引用

页码：537 / 553

页数：17

共 28 条

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