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This paper introduces a novel approach for face recognition using multiple face patterns obtained in various views for robot vision. A face pattern may change dramatically due to changes in the relation between the positions of a robot, a subject and light sources. As a robot is not generally able to ascertain such changes by itself, face recognition in(More)
This paper introduces the kernel constrained mutual sub-space method (KCMSM) and provides a new framework for 3D object recognition by applying it to multiple view images. KCMSM is a kernel method for classifying a set of patterns. An input pattern x is mapped into the high-dimensional feature space F via a nonlinear function φ, and the mapped pattern φ(x)(More)
This paper proposes the kernel orthogonal mutual subspace method (KOMSM) for 3D object recognition. KOMSM is a kernel-based method for classifying sets of patterns such as video frames or multi-view images. It classifies objects based on the canonical angles between the nonlinear subspaces, which are generated from the image patterns of each object class by(More)
In this paper, we propose a novel method named the Multiple Constrained Mutual Subspace Method which increases the accuracy of face recognition by introducing a framework provided by ensemble learning. In our method we represent the set of patterns as a low-dimensional subspace, and calculate the similarity between an input subspace and a reference(More)
SUMMARY In this paper, we propose a method for fast and accurate extraction of feature points such as pupils, nostrils, mouth edges, and the like from dynamic images with the purpose of face recognition. Accuracy of face extraction with these feature points used as criteria greatly affects the capabilities of face recognition methods based on pattern(More)
This paper presents a novel algorithm for estimating stereo disparity which exploits the benefit of learning to the fullest. Given a cost volume of stereo matching, we solve the cost aggregation and disparity computation in one shot by using a classifier; we design a feature called matching cost pattern for the input which we extract from the cost volume(More)
We present a lip contour extraction method using sep-arability of color intensity distributions. Usually it is difficult to robustly extract the outer lip contour mainly because of the following two problems. First, the outer lip contour is often blurred. Secondly, the contrast between the skin and the lip region is often reduced by transformation from the(More)
Object recognition using image-set or video sequence as input tends to be more robust since image-set or video sequence provides much more information than single snapshot about the variability in the appearance of the target subject. Constrained Mutual Subspace Method (CMSM) is one of the state-of-the-art algorithms for image-set based object recognition(More)