Segmentation of human face using gradient-based approach

This paper describes a method for automatic segmentation of facial features such as eyebrows, eyes, nose, mouth and ears in colour images. This work is an initial step for wide range of applications based on feature-based approaches, such as face recognition, lip-reading, gender estimation, facial expression analysis, etc. Human face can be characterised by its skin colour and nearly elliptical shape. For this purpose, face detection is performed using colour and shape information. Uniform illumination is assumed. No restrictions on glasses, mace-up, beard, etc, are imposed. Facial features are extracted using the vertically and horizontally oriented gradient projections. The gradient of a minimum with respect to its neighbour maxima gives the boundaries of a facial feature. Each facial feature has a different horizontal characteristic. These characteristics are derived by extensive experimentation with many face images. Using fuzzy set theory, the similarity between the candidate and the feature characteristic under consideration is calculated. Gradient-based method is accompanied by the anthropometrical information, for robustness. Ear detection is performed using contour-based shape descriptors. This method detects the facial features and circumscribes each facial feature with the smallest rectangle possible. AR database is used for testing. The developed method is also suitable for real-time systems.