This paper deals with the low-level joint processing of color and motion for robust face analysis within a feature-based approach. To gain robustness and contrast under unsupervised viewing conditions, a nonlinear color transform relevant for hue segmentation is derived from a logarithmic model. A hierarchical segmentation scheme is based on Markov random field modeling, that combines hue and motion detection within a spatiotemporal neighborhood. Relevant face regions are segmented without parameter tuning. The accuracy of the label fields enables not only face detection and tracking but also geometrical measurements on facial feature edges, such as lips or eyes. Results are shown both on typical test sequences and on various sequences acquired from micro- or mobile cameras. The efficiency of the method makes it suitable for real-time applications aiming at audiovisual communication in unsupervised environments.

An unsupervised framework for face analysis aiming at lip tracking is presented in this paper. A colour video sequence of a speaker's face is simply acquired by a desktop camera under natural lighting conditions and without any particular make-up. After a logarithmic colour transform, a statistical segmentation process regularizes motion and hue information within a spatio-temporal neighbourhood. The hierarchical segmentation labels the different areas of the face. Results are then used to define a region of interest for each feature in the face, particularly the lip contours. Lip corners and associated characteristic points are extracted to initialise an active contours stage. Finally, a speaker's lip shape with inner and outer borders is tracked without user tuning: This unsupervised framework provides geometrical features of the face when no specific model of the speaker face is assumed.

An unsupervised algorithm for speaker's lip segmentation is presented. A color video sequence of the speaker's face is acquired, under natural lighting conditions and without any particular make-up. First, a logarithmic color transform is performed from the RGB to HI (hue, intensity) color space and sequence dependant parameters are evaluated. Second, a statistical approach using Markov random field modeling segment the mouth shape using the red hue predominant region and motion in a spatiotemporal neighborhood. Simultaneously, a region of interest (ROI) is automatically extracted. Third, the speaker's lip shape is extracted from the final hue field with good quality results in this challenging situation.