Improved Dynamic Threshold Method for Skin Colour Detection Using Multi-Colour Space

Abstract

This paper presents a skin colour detection based on an improved dynamic threshold method to reduce false skin detection. Current fixed threshold skin detection fails in certain situations such as misclassification between non skin-like with similar skin-like colour. Any true skin may falsely be detected as non-skin. Research work introduces high-level skin detection strategy based on online sampling where offline training is not required. This strategy shows a promising performance in term of classifying images under skin-like and ethnicity image variations. However, some of the methods produced high false positives that reduced the accuracy of skin detection performance. Therefore, in this study, instead of single colour space and fixed threshold method, an improved skin detection based on multi-colour spaces is proposed. Further more, a dynamic threshold method also has been improved by introducing elastic elliptical mask model for online skin sampling. The experimental result shows an improvement in employing multi-colour rather than single colour space by reducing the false positive and increasing the precision rate.