Multi-feature face recognition based on PSO-SVM

Face recognition is a kind of identification and authentication, which mainly use the global-face feature. Nevertheless, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face f...

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Bibliographic Details
Main Authors: Valuvanathorn, S., Nitsuwat, S., Mao Lin Huang
Format: Conference Proceeding
Language:English
Subjects:
2D-PCA
Accuracy
Face
Face recognition
global-face feature
Kernel
local-face feature
Nose
PCA
PHB
Principal component analysis
PSO
Support vector machines
SVM
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Summary:Face recognition is a kind of identification and authentication, which mainly use the global-face feature. Nevertheless, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with 4 parts: the left-eye, right-eye, nose and mouth. We used 115 face images from BioID face dataset for learning and testing. Each-individual person's images are divided into 3 different images for training and 2 different images for testing. The processed histogram based (PHB), principal component analysis (PCA) and two-dimension principal component analysis (2D-PCA) techniques are used for feature extraction. In the recognition process, we used the support vector machine (SVM) for classification combined with particle swarm optimization (PSO) to select the parameters G and C automatically (PSO-SVM). The results show that the proposed method could increase the recognition accuracy rate.
ISSN:2157-0981
2157-099X
DOI:10.1109/ICTKE.2012.6408543