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In a recent study a novel classification algorithm called the Sparse Classifier (SC) assumes that if a test sample belongs to class k then it can be approximately represented by a linear combination of the training samples belonging to k. Good face recognition results were obtained by the SC method. This paper proposes two generalizations of the aforesaid assumption. The first generalization assumes...
In this paper we propose a new approach to video based face recognition. Our work is based on the Sparse Classification approach which assumes that each test sample can be formed by a linear combination of the training samples of the correct class. Based on this assumption, we formulate the classification problem as one of joint sparse recovery of Multiple Measurement Vectors (MMV). This requires...
SIFT (Scale Invariant Feature Transform) features are widely used in object recognition. These features are invariant to changes in scale, 2D translation and rotation transformations. To a limited extent they are also robust to 3D projection transformations. SIFT Features however, are of very high dimension and large number of SIFT features are generated from an image. The large computational effort...
Recently a new classification assumption was proposed in [1]. It assumed that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The classification algorithm in [1] was based on the idea that all the correlated training samples belonging to the correct class are used to represent the test sample. The Lasso regularization was proposed...
This paper addresses the problem of identifying faces when the training face database consists of one face image of each person. It proposes a new approach that synthesizes new face samples of varying degrees of edge information; the synthesized images are generated from the original image and form non-linear approximations of the latter. The approximation is framed as an l1 minimization problem...
The problem of recognizing a face from a single sample available in a stored dataset is addressed. A new method of tackling this problem by using the Fisherface method on a generic dataset is explored. The recognition scheme is also extended to multiscale transform domains like wavelet, curvelet and contourlet. The proposed method in the transform domain shows better recognition errors than the SPCA...
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