Chunghoon Kim

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Illumination variation that occurs on face images degrades the performance of face recognition. In this paper, we propose a novel approach to handling illumination variation for face recognition. Since most human faces are similar in shape, we can find the shadow characteristics, which the illumination variation makes on the faces depending on the direction(More)
— This paper proposes a combined subspace method using both global and local features for face recognition. The global and local features are obtained by applying the LDA-based method to either the whole or part of a face image, respectively. The combined subspace is constructed with the projection vectors corresponding to large eigenvalues of the(More)
In manipulating data such as in supervised learning, we often extract new features from the original input variables for the purpose of reducing the dimensions of input space and achieving better performances. In this paper, we show how standard algorithms for independent component analysis (ICA) can be extended to extract attributes for regression(More)
In this paper, we propose a new discriminant analysis using composite features for pattern classification. A composite feature consists of a number of primitive features, each of which corresponds to an input variable. The covariance of composite features is obtained from the inner product of composite features and can be considered as a generalized form of(More)
We propose a new biased discriminant analysis (BDA) using composite vectors for eye detection. A composite vector consists of several pixels inside a window on an image. The covariance of composite vectors is obtained from their inner product and can be considered as a generalization of the covariance of pixels. The proposed composite BDA (C-BDA) method is(More)