A REVIEW OF SKIN DETECTOR BASED DEEP LEARNING TECHNIQUES: COHERENT TAXONOMY, OPEN CHALLENGES, MOTIVATIONS, RECOMMENDATIONS AND STATISTICAL ANALYSIS, FUTURE DIRECTION

This paper review and analysis the literature on skin detector (SD), in order to establish the coherent taxonomy and figure out the gap on this pivotal research area. An extensive search is conducted to identify articles that deal with skin detection, skin segmentation, skin tone detector, and skin recognition issues, related techniques are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore, and Web of Science databases are checked for articles on skin detector. A total of 2803 papers are collected from 2007 to February 2018. The set comprised 173 articles. The largest portion of the papers (n = 158/173) = 91% belong to Development and Design, that is aimed to develop an approach for skin classifier into skin and non-skin. A sum total of (n = 5/173) = 3% of the papers belong to Evaluation and Framework, (n = 10/173) = 6% papers was categorized as Comparative Study. This paper discusses the open challenges, motivations and recommendations of the related works. Furthermore, state-of-the-art is a step to demonstrate the novelty of the presented study by conducted a statistical analysis for previous studies such as (Dataset, Color spaces, features, image type, and Classification techniques) as a future direction for other researchers who are interested in SD.