Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images

TL;DR

This study compares three skin detection algorithms on Indian sub-continental images, finding HSV-based methods most effective with over 90% accuracy, addressing a gap in ethnicity-specific skin detection research.

Contribution

It introduces a comparative analysis of skin detection algorithms specifically for Indian sub-continental images, optimizing detection parameters for this ethnicity.

Findings

HSV-based approach achieved 91.1% true positives.

HSV approach had 88.1% true negatives.

HSV method outperformed other segmentation approaches.

Abstract

Object detection has been a focus of research in human-computer interaction. Skin area detection has been a key to different recognitions like face recognition, human motion detection, pornographic and nude image prediction, etc. Most of the research done in the fields of skin detection has been trained and tested on human images of African, Mongolian and Anglo-Saxon ethnic origins. Although there are several intensity invariant approaches to skin detection, the skin color of Indian sub-continentals have not been focused separately. The approach of this research is to make a comparative study between three image segmentation approaches using Indian sub-continental human images, to optimize the detection criteria, and to find some efficient parameters to detect the skin area from these images. The experiments observed that HSV color model based approach to Indian sub-continental skin detection is more suitable with considerable success rate of 91.1% true positives and 88.1% true negatives.