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• W2073011137 abstract "표정은 인간의 감정을 전달할 수 있는 중요한 수단으로 표정 인식은 감정상태를 알아낼 수 있는 효과적인 방법중 하나이다. 일반적인 표정 인식 시스템은 얼굴 표정을 표현하는 특징점을 찾고, 물리적인 해석 없이 특징을 추출한다. 하지만 특징점 추출은 많은 시간이 소요될 뿐 아니라 특징점의 정확한 위치를 추정하기 어렵다. 그리고 표정 인식 시스템을 실시간 임베디드 시스템에서 구현하기 위해서는 알고리즘을 간략화하고 자원 사용량을 줄일 필요가 있다. 본 논문에서 제안하는 실시간 표정 인식 시스템은 격자점 위치에서 얻어진 가버 웨이블릿(Gabor wavelet) 특징 기반 표정 공간을 설정하고, 각 표정 공간에서 얻어진 주성분을 신경망 분류기를 이용하여 얼굴 표정을 분류한다. 제안하는 실시간 표정 인식 시스템은 화남, 행복, 평온, 슬픔 그리고 놀람의 5가지 표정이 인식 가능하며, 다양한 실험에서 평균 10.25ms의 수행시간, 그리고 87%~93%의 인식 성능을 보였다. Human emotion can be reflected by their facial expressions. So, it is one of good ways to understand people's emotions by recognizing their facial expressions. General recognition system of facial expressions had selected interesting points, and then only extracted features without analyzing physical meanings. They takes a long time to find interesting points, and it is hard to estimate accurate positions of these feature points. And in order to implement a recognition system of facial expressions on real-time embedded system, it is needed to simplify the algorithm and reduce the using resources. In this paper, we propose a real-time recognition algorithm of facial expressions that project the grid points on an expression space based on Gabor wavelet feature. Facial expression is simply described by feature vectors on the expression space, and is classified by an neural network with its resources dramatically reduced. The proposed system deals 5 expressions: anger, happiness, neutral, sadness, and surprise. In experiment, average execution time is 10.251 ms and recognition rate is measured as 87~93%." @default.
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• W2073011137 date "2009-12-25" @default.
• W2073011137 modified "2023-09-26" @default.
• W2073011137 title "Real-time Recognition System of Facial Expressions Using Principal Component of Gabor-wavelet Features" @default.
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• W2073011137 doi "https://doi.org/10.5391/jkiis.2009.19.6.821" @default.
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