| ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
J.-C. Terrillon and M. N. Shirazi and M. Sadek and H. Fukamachi and S. Akamatsu
ABSTRACT
This paper presents an analysis of the performance of Support Vector Machines (SVMs) for the automatic detection of human faces in static color images of complex scenes. SVMs are a new interesting type of binary classifier based on a novel statistical learning technique that has been developed in recent years by V. Vapnik et al. at AT&T Bell Labs [2] [4] [6] [22]. Skin color-based image segmentation is initially performed for several different chrominance spaces by use of the single Gaussian chrominance model and of a Gaussian mixture density model, as described in [17]. Feature extraction in the segmented images is then implemented by use of invariant Orthogonal Fourier-Mellin Moments (OFMMs) [16] [20]. For all chrominance spaces, the application of SVMs to the invariant moments obtained from a set of 100 test images yields a higher face detection performance than when applying a 3-layer perceptron Neural Network (NN), depending on a suitable selection of the kernel function used to train the SVM and of the value of its associated parameter(s). The training of SVMs is easier and faster than that of a NN, always finds a global minimum, and SVMs have a better generalization ability [5]. |
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