Adaptive Color Space Switching Based Approach for Face Tracking

In this paper, a support vector machine (SVM) based adaptive color switching for human face tracking is proposed. The color space is switching to the most appropriate color space model (CSM) according to circumstance conditions adaptively. Recently, many face tracking algorithms used empirical skin color model to discriminate skin/non-skin regions. These skin color models not consider illumination variation and result in less capacity to model skin color distribution. In this work, four color spaces and Laws texture extracted from face image database are used to train each SVM independently. In the pre-processing, the discrete wavelet transform (DWT) refines the face features would concentrate important features and reduce the computational complexity. Then, the features are transformed into four CSMs for SVMs which provide good generalization through optimal hyperplane. In testing, we perform quality measurement method to evaluate the face tracking performance and aggregating each SVM classification results to color space switching. Experimental results show that the proposed method would switch to the most appropriate color space according to quality measurement, automatically.