• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.107-115
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• 2008

Face feature detection plays an important role in application such as automatic speechreading, human computer interface, face recognition, and face image database management. We proposed a automatic speechreading feature detection algorithm for color image using color information. Face feature pixels is represented for various value because of the luminance and chrominance in various color space. Face features are detected by amplifying, reducing the value and make a comparison between the represented image. The eye and nose position, inner boundary of lips and the outer line of the tooth is detected and show very encouraging result.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.135-140
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• 2009

본 논문은 한국어 8 단모음을 인식하는 자동 독화 신경망 시스템을 구축한 것이다. 얼굴의 특정들은 휘도와 채도 성분으로 인하여 다양한 색 공간에서 다양한 표현 값을 갖는다. 이를 이용하여 각 표현 값들을 증폭하거나 축소, 대비시킴으로서 얼굴 특정들을 추출되게 하였다. 눈과 코, 안쪽 입의 외곽선, 이의 외곽선을 찾았고, 그 후 한국어 8모음 발화시 구분되게 변화는 값들을 파라미터로 설정하였다. 한국어 8모음을 발화하는 2400개의 자료를 모아 분석하고 이 분석을 바탕으로 신경망 시스템을 구축하여 실험하였다. 이 실험에 정상인 5명이 동원되었고, 사람들 사이에 있는 관찰 오차를 정규화를 통하여 수정하였다. 5명으로 분석하였고, 5명으로 인식 실험하여 좋은 결과를 얻었다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.173-182
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• 2009

In this paper, we studied about the extraction of the parameter and implementation of speechreading system to recognize the Korean 8 vowel. Face features are detected by amplifying, reducing the image value and making a comparison between the image value which is represented for various value in various color space. The eyes position, the nose position, the inner boundary of lip, the outer boundary of upper lip and the outer line of the tooth is found to the feature and using the analysis the area of inner lip, the hight and width of inner lip, the outer line length of the tooth rate about a inner mouth area and the distance between the nose and outer boundary of upper lip are used for the parameter. 2400 data are gathered and analyzed. Based on this analysis, the neural net is constructed and the recognition experiments are performed. In the experiment, 5 normal persons were sampled. The observational error between samples was corrected using normalization method. The experiment show very encouraging result about the usefulness of the parameter.