Comprehensive analysis of a diverse group of features and development of vision-based two-level hand detector under practical environment conditions

Abstract

Developing a bare-hand detection system for practical environment conditions is a complex and challenging task. Factors such as change in appearance, uneven illumination, and complex background add up to the difficulty in detecting the target hand. Present study newly explored 13 colour-texture and integrates them with texture models to develop robust two-level hand detector under practical conditions mentioned above. Colour-texture and texture models are assessed using multiple classification tools and employed in two subsequent levels such that the second level only classifies the optimal sub-windows classified in the first level. The analysis showed that the proposed two-level detection system detects the hand with 53.4% higher accuracy than the baseline model which the integrated motion detection and skin filtering method, under the practical conditions. With five times lower time-complexity than the baseline model, the proposed system can be used to detect hand in both static as well as dynamic gesture systems.