Using self-organizing fuzzy network with support vector learning for face detection in color images

Abstract

This paper proposes a three-stage face detection method using self-organizing Takagi–Sugeno (T–S)-type fuzzy network with support vector (SOTFN-SV) learning. SOTFN-SV is a T–S-type fuzzy system constructed by hybridizing fuzzy clustering and support vector machine. The proposed face detection method consists of three stages. In the first stage, SOTFN-SV is applied to skin color segmentation. Color information from the hue and saturation (HS) color space is used. In the second stage, face size and shape filters are employed to exclude some face candidates to reduce the number of false alarms. Shape analysis is based on the fact that an oval face shape can be approximated by an elliptical shape, and a best fitting ellipse to each connected skin region is found for analysis. In the final stage, colors of the eyes, mouth, and face skin regions of the remaining face candidates are used as detection features. An SOTFN-SV color filter uses these features as inputs to make a final detection decision. The proposed method has a fast detection speed and detects not only the face, but also its size and orientation. Experimental results verify the efficiency and effectiveness of the proposed face detection method.