Skin detection plays an important role in many applications, including face detection, human motion analysis, and objectionable image filtering. We propose a novel skin detection approach named multiple Gaussian models (MGMs). This approach combines multiple single Gaussian models and determines each model in order to maximize the true positive rate (TPR) of skin detection subject to a fixed predefined false positive rate (FPR). We derive the discrete and continuous forms of MGM approaches in the paper. The proposed approach has almost optimal performance for a broad range of FPRs in the Gaussian framework. Moreover, it has low computational costs in skin detection for new image instances. Experimental results show that the MGM approach has better skin detection performance than previous methods within the Gaussian framework.