• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.311-318
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• 2002

In this paper, we propose an efficient method for recognizing pupils and lip in a human face. Pupils are detected by a cost function, which uses features based on the eye's shape and a relation between pupil and eyebrow. The inner boundary of lip is detected by weighted vectors based on lip's shape and on the difference of gray level between lip and face skin. These vectors extract four feature points of lip : the top of the upper lip, the bottom of the lower lip, and the two corners. The experiments have been performed for many images and show very encouraging result.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.167-168
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• 2007

본 논문에서는 얼굴, 지문 등의 생체특징을 안전하게 은닉하고 효과적으로 은닉정보를 추출할 수 있는 웨이블렛 기반 워터마킹 기법을 제안한다. 제안된 방법은 웨이블렛을 이용하여 워터마크 삽입위치를 결정하고 웨이블렛 변환된 영상과 배경영상간의 차와 삽입위치 주변의 영상에 분산값을 이용해 퍼지 함수를 이용하여 적응적 가중치 값을 결정한다. 은닉된 워터마크 데이터는 워터마크가 삽입된 영상에 웨이블렛 변환을 적용하여 효과적으로 생체특징을 추출한다. 제안된 방법의 타당성을 검증하기 위하여 워터마크 데이터인 생체특징의 은닉 전과 후의 특성분석과 워터마크 알고리즘이 생체 인식시스템에 미치는 영향을 평가하였다. 실험한 결과 제안된 방법은 효과적으로 생체정보를 은닉하고 생체인식률의 저하 없이 효과적으로 생체정보를 보호할 수 있음을 확인 할 수 있었다.

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.553-561
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• 2010

This paper proposed the face tracking method which can be effectively applied to the robot's vision system. The proposed algorithm tracks the facial areas after detecting the area of video motion. Movement detection of video images is done by using median filter and erosion and dilation operation as a method for removing noise, after getting the different images using two continual frames. To extract the skin color from the moving area, the color information of sample images is used. The skin color region and the background area are separated by evaluating the similarity by generating membership functions by using MIN-MAX values as fuzzy data. For the face candidate region, the eyes are detected from C channel of color space CMY, and the mouth from Q channel of color space YIQ. The face region is tracked seeking the features of the eyes and the mouth detected from knowledge-base. Experiment includes 1,500 frames of the video images from 10 subjects, 150 frames per subject. The result shows 95.7% of detection rate (the motion areas of 1,435 frames are detected) and 97.6% of good face tracking result (1,401 faces are tracked).

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.173-178
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• 2014

In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.605-613
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• 2015

This paper addresses the problem to make sparse the projection matrix in pattern recognition method. Recently, the size of computer program is often restricted in embedded systems. It is very often that developed programs include some constant data. For example, many pattern recognition programs use the projection matrix for dimension reduction. To improve the recognition performance, very high dimensional feature vectors are often extracted. In this case, the projection matrix can be very big. Recently, RSR(roated sparse regression) method[1] was proposed. This method has been proved one of the best algorithm that obtains the sparse matrix. We propose three methods to improve the RSR; outlier removal, sampling and elastic net RSR(E-RSR) in which the penalty term in RSR optimization function is replaced by that of the elastic net regression. The experimental results show that the proposed methods are very effective and improve the sparsity rate dramatically without sacrificing the recognition rate compared to the original RSR method.