This paper presents an analysis of the performance of two different skin chrominance models and of nine different chrominance spaces for the color segmentation and subsequent detection of human faces in two-dimensional static images. For each space, we use the single Gaussian model based on the Mahalanobis metric and a Gaussian mixture density model to segment faces from scene backgrounds. In the case of the mixture density model, the skin chrominance distribution is estimated by use of the ExpectationMaximisation (EM) algorithm [14]. Feature extraction is performed on the segmented images by use of invariant Fourier-Mellin moments [21]. A multilayer perceptron neural network (NN), with the invariant moments as the input vector, is then applied to distinguish faces from distractors. With the single Gaussian model, normalized color spaces are shown to produce the best segmentation results, and subsequently the highest rate of face detection. The results are comparable to those obtained with the more sophisticated mixture density model. However, the mixture density model improves the segmentation and face detection results significantly for most of the un-normalized color spaces. Ultimately, we show that, for each chrominance space, the detection efficiency depends on the capacity of each model to estimate the skin chrominance distribution and, most importantly, on the discriminability between skin and "non-skin" distributions.

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We use a skin color model based on the Mahalanobis metric and a shape analysis based on invariant Fourier-Mellin moments to automatically detect and locate human faces in two-dimensional complex scene images. First, color segmentation of an input image is performed by thresholding in a normalized hue-saturation color space where the effects of the variability of human skin color and the dependency of chrominance on changes in illumination are reduced. We then group regions of the resulting binary image that have been classified as face candidates into clusters of connected pixels. Discarding the smallest clusters in the image ensures that only a small number of clusters will be used for further analysis. Fully translation-, scale-and in-plane rotationinvariant moments are calculated for each remaining cluster. Finally, in order to distinguish faces from distractors, a multilayer perceptron neural network is used with the invariant moments as the input vector. Supervised learning of the network is implemented with the backpropagation algorithm, at first for frontal views of faces. Preliminary results show the efficiency of the combination of color segmentation and of invariant moments in detecting faces with a large variety of poses and against relatively complex backgrounds.

In a two-person perfect-information game, Conspiracy Number Search (CNS) was invented as a possible search algorithm but did not find much success. However, we believe that the conspiracy number, which is the core of CNS, has not been used to its full potential. In this paper, we propose a novel way to utilize the conspiracy number in the minimax framework. Instead of using conspiracy numbers separately, we combine them together. An example way of combining conspiracy numbers with the evaluation value is suggested. Empirical results obtained for the game of Othello show the potential of the proposed method. 2 RELATED WORKS In this section, we describe in more details about conspiracy numbers and their application in previous 400

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