2D/3D Face Recognition Using Neural Network Based on Hybrid Taguchi-Particle Swarm Optimization

Please use this identifier to cite or link to this item: http://ir.lib.ncut.edu.tw/handle/987654321/6872

Title:	2D/3D Face Recognition Using Neural Network Based on Hybrid Taguchi-Particle Swarm Optimization
Authors:	林正堅
Contributors:	資訊工程系
Date:	2011-02
Issue Date:	2017-12-17 13:09:55 (UTC+8)
Abstract:	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 (PGA) 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. ICIC International © 2011. (28 refs)
Relation:	International Journal of Innovative Computing, Information and Control (IJICIC)
Appears in Collections:	[Department of Computer Science and Information Engineering] 【資訊工程系所】期刊論文

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