A Hybrid Cascade Approach for Human Skin Segmentation
Journal of Advances in Mathematics and Computer Science · pp. 1–14 · Published 29 Jul 2016
10.9734/BJMCS/2016/26412Abstract
Human skin segmentation is fundamental to a wide range of computer vision applications ranging from face recognition, facial expression recognition and gesture analysis to various human computer interaction domains. In this paper, we propose a multistage skin segmentation method built as a cascade of a nonparametric generic model and an adaBoost classifier. Several entities are used to train the adaBoost classifier. Feature vectors fed into the ada Boost contain color information from two different color spaces. Extensive experiments are conducted on two datasets in order to evaluate the performance of the approach. The various results obtained show that the proposed method is a promising approach and it successfully achieves high quality segmentation, while concurrently retaining reasonably low false alarm rates. The comparison of the proposed method with related state-of-the-art competitors reveals the superiority and effectiveness of the proposed method, while maintaining real-time performance.
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