A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

Abstract

Skin detection involves identifying skin and non-skin areas in a digital image and is commonly used in various applications, such as analyzing hand gestures, tracking body parts, and facial recognition. The process of distinguishing between skin and non-skin regions in a digital image is widely used in a variety of applications, ranging from hand-gesture analysis to body-part tracking to facial recognition. Skin detection is a challenging problem that has received a lot of attention from experts and proposals from the research community in the context of intelligent systems, but the lack of common benchmarks and unified testing protocols has hampered fairness among approaches. Comparisons are very difficult. Recently, the success of deep neural networks has had a major impact on the field of image segmentation detection, resulting in various successful models to date. In this work, we survey the most recent research in this field and propose fair comparisons between approaches, using several different datasets. The main contributions of this work are (i) a comprehensive review of the literature on approaches to skin-color detection and a comparison of approaches that may help researchers and practitioners choose the best method for their application; (ii) a comprehensive list of datasets that report ground truth for skin detection; and (iii) a testing protocol for evaluating and comparing different skin-detection approaches. Moreover, we propose an ensemble of convolutional neural networks and transformers that obtains a state-of-the-art performance.