Skin detection is used in applications in computer vision, including image correction, image–content filtering, image processing, and skin classification. In this study, we propose an accurate and effective method for detecting the most representative skin color in one's face based on the face's center region, which is free from nonskin-colored features, such as eyebrows, hair, and makeup. The face's center region is defined as the region horizontally between the eyes and vertically from the middle to the tip of one's nose. The performance of the developed algorithm was verified with a data set that includes more than 300 facial images taken under various illuminant conditions. Compared to previous works, the proposed algorithm resulted in a more accurate skin color detection with reduced computational load.
This paper presents an algorithm to detect skin pixels in an image. Each pixel is classified as a skin or non-skin pixel based on features extracted from its neighborhood. The presented algorithm uses a modified likelihood ratio for classification, and uses a multi-scale approach to classify the pixel in question. The algorithm was developed and evaluated using the ColorFERET dataset. The presented algorithm achieved 95.6 % classification accuracy.