Human Face Detection Using Skin Color Context Awareness and Context-Based Bayesian Classifiers

Abstract

We propose a cascade detection scheme by combining the color feature-based method and appearance-based method. In addition, the scheme employs illumination context-awareness so that the detection scheme can react in a robust way against dynamically changing illumination. Skin color provides rich information for extracting rough area of the face. Difficulties in detecting face skin color come from the variations in ambient light, image capturing devices, etc,. Appearance-based object detection, multiple Bayesian classifiers here, is attractive since it could accumulate object models by autonomous learning process. This approach can be easily adopted in searching for multiple scale faces by scaling up/down the input image with some factor. The appearance-based method shows more stability under changing illumination than other detection methods, but it is still bordered from the variations in illumination. We employ Fuzzy ART and RBFN for the illumination context- awareness. The proposed face detection achieves the capacity of the high level attentive process by taking advantage of the illumination context-awareness in both color feature-based detection and multiple Bayesian classifiers. We achieve very encouraging experimental results, especially when illumination condition varies dynamically.