Higher order symmetry for non-linear classification of human walk detection
Pattern Recognition Letters
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Feature extraction for cancer classification using kernel-based methods
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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This paper presents a modification of kernel-based Fisher Discriminant Analysis (FDA) for face detection. In face detection problem, it is important to design a two-category classifier which can decide whether the given input sub-image is a face or not. There is a difficulty to train such tow-category classifiers because the 驴non face驴 class includes many images of different kinds of objects and it is difficult to treat them as a single class. Also the dimension of the discriminant space constructed by the usual FDA is limited to 1 for tow-category classification. To overcome these problems of the usual FDA, the discriminant criterion of the usual FDA is modifed such that the covariance of the 驴face驴 class is minimized while the differences between the center of the 驴face驴 class and each training sample of the 驴non face驴 class are maximized. By this modification we can obtain a higher dimensional discriminant space which is suitable for 驴face驴 and 驴not face驴 classification. It is shown that the proposed method could outperform the support vector machine (SVM) by experiments of 驴face驴 and 驴non face驴 classification using the face images gathered from the available face database and the many face images on the Web.Keywors: kernel-based Fisher discriminant analysis, face detection, kernel-based principal component analysis, support vector machine