Jean-Christophe Terrillon

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Color is a powerful fundamental cue that can be used at an early stage to detect objects in complex scene images. This paper presents an analysis of the performance of nine different chrominance spaces in the specific problem of automatically detecting and locating human faces in two-dimensional still scene images. For each space, we use a skin color model(More)
We use a skin color model based on the Mahalanobis metric and a shape analysis based on invariant moments to automatically detect and locate human faces in two-dimensional natural scene images. First, color segmentation of an input image is performed by thresholding in a perceptually plausible hue-saturation color space where the effects of the variability(More)
Human skin color is a powerful fundamental cue that can be used in particular, at an early stage, for the important applications of face and hand detection in color images [l] [2], and ultimately, for meaningful human-computer interactions. In this paper, we analyze the distribution of human skin for a large number of chrominance spaces and for skin images(More)
In this paper, we apply a recently developed type of moments , Orthogonal Fourier-Mellin Moments (OFMMs) [7], to the specijic problem of fully translation-, scale-and in-plane rotation-invariant detection of human faces in two-dimensional static color images, and we compare theirper-formance with that of the generalized Hu's moments or non-orthogonal(More)