NDLI: A PSO- SVM Lips Recognition Method Based on Active Basis Model

Content Provider	IEEE Xplore Digital Library
Author	Chih-Yu Hsu Chih-Hung Yang Yung-Chih Chen Min-chian Tsai
Copyright Year	2010
Abstract	The paper proposed a novel method for lip recognition based on Active Basis Model (ABM). There are four stages in a flowchart of this novel method. At the first stage the deformable templates of lip images is obtained. The lip images of deformable templates are obvious open or closed. The second stage is to obtain the deformed template of each testing images. The third stage, the difference between the deformable template and deformed template is calculated and used as a feature vector. Finally, the support vector machine (SVM) is use to classify the feature vector. SVM is a supervised learning to be a classifier for lip recognition. It is necessary to set and parameters, which are the two factors affecting quality of the model, when establishing SVM method. Hence, in terms of parameters selections, the researcher adopted PSO (Particle Swarm Optimization, PSO) algorithm in this research to set the best parameters combination and then incorporated into the SVM to obtain the classified results. In this paper, the novel method which utilizes PSO algorithm to select the parameters and automatically is called PSO-SVM method. There are 1000 face images in BioID face database used for the experiment. The experimental results show that PSO-SVM method can be a more accurate model to recognize the lip images.
Starting Page	743
Ending Page	747
File Size	426589
Page Count	5
File Format	PDF
ISBN	9781424488919
DOI	10.1109/ICGEC.2010.188
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lip images Accuracy Lips Computational modeling Active basis model Support vector machine classification Classification algorithms Support vector machine Particle swarm optimization Testing
Content Type	Text
Resource Type	Article

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