Browsing by Author "Chien, Shuo-Cheng"

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Facial Expression Recognition Based on Mixture of Basic Expressions and Intensities
(2012-01-01)
Song, Kai-Tai
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Chien, Shuo-Cheng
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
Facial expression recognition can provide rich emotional information for human-robot interaction. This paper presents a facial expression recognition design that recognizes facial expressions as well as intensity and mixture ratio of six basic facial expressions. In this system, Active Appearance Model (AAM) and Lucas-Kanade image alignment algorithms are adopted to align the input facial images to obtain texture features. A novel method is proposed to recognize mixture ratio of basic facial expressions and the intensity of the expression. Three kinds of texture features are used in this method: 1. texture features of the whole face, which are used as inputs of facial expression intensity recognition; 2. texture features of the upside face, which are used as inputs of upper face action units recognition; 3. texture features of the downside face, which are used as the inputs of lower face action units recognition. Back propagation neural networks are used to obtain the recognition scores, which are then exploited to classify the facial expression results. Experimental results verified that the proposed method can effectively recognize mixture ratio of six basic expressions and the expression intensity.
User Identification Design by Fusion of Face Recognition and Speaker Recognition
(2012-01-01)
Lin, Chao-Yu
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Song, Kai-Tai
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Chen, Yi-Wen
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Chien, Shuo-Cheng
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Chen, Sin-Horng
Intelligent human-robot interface helps a mobile robot to extract external information and interact with a user. User identification information allows a robot to generate appropriate behaviors and make personalized human-robot interaction (PHRI) more natural and safe. Most of service robots move around in various application settings, and biometric features always change during relative movements. In this work, a fusion recognition system based on face and speaker classifiers is designed to achieve the user recognition in various environments. We propose a method to fuse face and speech recognition results based on the concept of confidence indices. Through the derived confidence indices, the system combines the two classifiers and gives improved recognition results. Experiments on a laboratory home service robot show that person recognition rates in 15 environmental situations all outperform those of using single modality.