Automatic image annotation using adaptive color classification
Graphical Models and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards Segmentation from Multiple Cues: Symmetry and Color
Proceedings of the 10th International Workshop on Theoretical Foundations of Computer Vision: Multi-Image Analysis
Saliency model-based face segmentation and tracking in head-and-shoulder video sequences
Journal of Visual Communication and Image Representation
A novel fuzzy rule base system for pose independent faces detection
Applied Soft Computing
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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.