An adaptive skin color detection algorithm with confusing backgrounds elimination

In this paper a new skin detection method based on adaptive thresholds is proposed. Compared with the fixed threshold histogram method used widely, ours can find optimal thresholds to the different complex backgrounds. Four clues are summarized from the skin probability distribution histogram (SPDH) to help search candidates of optimum thresholds, and an ANN classifier is trained to select the final optimum threshold. A color deviation histogram (CDH) is also proposed to eliminate confusing backgrounds and refine optimal thresholds. The selection process of optimal thresholds is fast thus appropriate for real-time applications since no iterative operation is involved. Experimental results show that the proposed method can achieve better performance than the fixed threshold histogram method.