肤色像素检测技术是成人图像识别、人脸识别等与人体相关的图像识别系统的基础和重要组成部分。为了提高肤色像素检测的准确度，本文提出一种模糊理论与FP神经网络（Forward Propagation Neural Network）相结合的肤色像素检测算法。算法首先通过模糊理论和直觉模糊理论提取待识别像素的颜色特征，构成特征向量，其中包括像素对常见肤色像素颜色值的隶属度和犹豫度，为完整的表达肤色像素的特征，再加入粗糙度特征进行补充；然后训练出FP神经网络，对所提取的特征向量进行肤色像素与非肤色像素的分类。实验证明，该算法能够提高肤色像素检测的准确度，可以有效地应用在有关人体的识别系统中。

Skin pixel detection technique is a basic and important part of image recognition system related to human being, such as adult image recognition, face recognition, etc. In order to improve the precision of skin pixel detection technique, a skin pixel detection algorithm that combines fuzzy theory with Forward Propagation(FP) Neural Network is proposed. The algorithm composes a feature vector based on extracted color features of the pixel through fuzzy theory and intuitionistic fuzzy theory. The feature vector includes membership and hesitancy degree of the pixel to common skin pixels. Roughness is a supplementary feature in the feature vector in order to completely express the feature of skin pixel. Then the algorithm is used to train a FP neural network and the feature vectors are classified into skin pixels and non-skin pixels. Experiment shows that the algorithm could improve the precision of skin detection and be effectively used in recognition systems related to human being.