NDLI: Development of a facial expression recognition system for the laughter therapy

Content Provider	IEEE Xplore Digital Library
Author	Yu-Jie Li Sun-Kyung Kang Young-Un Kim Sung-Tae Jung
Copyright Year	2010
Description	Author affiliation: Dept. of Computer Engineering, Wonkwang University, Iksan, Jeonbuk, Korea (Yu-Jie Li; Sun-Kyung Kang; Young-Un Kim; Sung-Tae Jung)
Abstract	This paper proposes a facial expression recognition system for the laughter therapy. The proposed system takes two steps: face detection and facial expression recognition. At the face detection stage, candidate facial areas are detected in real time from images taken by a camera in consideration of Haar-like features, followed by the application of a SVM(Support Vector Machine) classifier to detect face images in a more correct way. Next, histogram matching-based illumination normalization is used to mitigate the influence of lighting on the detected images. At the facial expression recognition stage, PCA (Principle Component Analysis) is used to capture features of the face, and real-time laugher recognition is made via a multi-layer perceptron artificial neural network. From the findings of this study, we conclude that the proposed method can improve facial expression recognition through illumination normalization based on histogram matching and by testing candidate facial images with a SVM.
Starting Page	168
Ending Page	171
File Size	2066504
Page Count	4
File Format	PDF
ISBN	9781424464999
e-ISBN	9781424465026
DOI	10.1109/ICCIS.2010.5518563
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-28
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Medical treatment Face detection Histograms Lighting Cameras Principal component analysis Multilayer perceptrons Artificial neural networks Image recognition perceptron arfiticial neural network facial expression recognition support vector machine histogram matching principal component analysis
Content Type	Text
Resource Type	Article

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