Taxonomy of Texture and Color-Texture Features for Developing Hand Detection System under Non-Ideal Conditions

Abstract

Bare hand detection system stages many challenging factors such as illumination, position and rotation variation, complex background etc. Present study explores 4 gray-level texture features and 5 color-texture features for robust detection of hand. In this paper, hand detection primarily aims to develop a 2D based hand gesture recognition system. Existing system uses red-markers, data-glove, and 3D cameras etc., which are not cost effective and user friendly for a realtime application. Present system uses a classification based approach using Naive Bayes, Real AdaBoost, Gentle AdaBoost and Modest AdaBoost for analyzing the notable information present in the features. AdaBoost is an efficient boosting algorithm primarily developed for binary classification. A bare hand detection system is effected by even a slight variation in the environmental conditions. Present study highlights the performance of the features under varying non-ideal conditions. An effective and flexible pair-wise comparative analysis method called Analytic Hierarchy Process (AHP) is used for pair-wise comparisons among classifiers and features. Results suggest that Modest AdaBoost is the most stable classifier among others, while Laplacian of Gaussian (LoG) based Gabor feature is the most efficient feature with the maximum accuracy of 97%. Features and classifiers are also evaluated based on time-complexity where Naive Bayes system has better training time-efficiency as compared to AdaBoost systems and color-texture features has less time complexity as compared to texture features due to lower dimensions.