Human skin detection: An unsupervised machine learning way.

Researchers have been involved for decades in search of an efficient skin detection method. However, current methods have not overcome the significant challenges of skin detection, such as variation of illumination, various skin tones of different ethnic groups, and many others. This research proposed a clustering and region-growing-based skin detection method to overcome these limitations. Together with significant insight, these methods result in a more effective algorithm. The insight concerns the capability to dynamically define the number of clusters in a collection of pixels organized as images. In Clustering for most problem domains, the number of clusters is fixed prior and does not perform effectively over a wide variety of data contents. Therefore, this research paper proposed a skin detection method that validated the above findings. The proposed method assigns the number of clusters based on image properties and ultimately allows freedom from manual thresholds or other manual operations. The dynamic determination of clustering outcomes allows for greater automation of skin detection when dealing with uncertain real-world conditions. • Human skin tone is uniform, and clustering is an efficient method to detect human skin. • CIELAB color space is better for separating illumination from the image. • A small number of significant objects can occupy most of the pixel in an image. • Pixel connectivity of an image can automatically decide the number of clusters. • Clustering performs efficiently in Human skin detection. [ABSTRACT FROM AUTHOR]