Multi-Level Analysis of Bit-Plane Based GLAC Feature and other Existing Texture Features for a Robust Hand Detection System

Abstract

Hand detection is the vital step towards developing a gesture recognition system. Robust hand detection is a challenging task and needs a deeper investigation of hand-oriented features under practical conditions. Existing texture features such as Histogram of oriented gradients (HOG), and Gabor feature are efficient but requires high extraction time due to their dense nature. If the feature is extracted from an edge-filtered imaged, only the vital edge features will be processed while reducing the computation and time complexity. Therefore present work proposes a bit-plane based feature extraction approach. Also, a new texture feature is proposed, Gradient Local Auto-Correlations (GLAC) that extracts the 2nd order statistical parameters such as curvature statistics unlike HOG, Gabor, and histogram feature. GLAC is also modified to GLACgrid feature to extract local texture feature by using spatial binning grids of $2\times 3$ with 5 orientation bins. Experimental observations showed that performance of GLACgrid feature is approx. 3.5%, 10.6%, and 19% higher than HOG, Gabor and histogram feature, respectively. Evaluation models are developed using Naive Bayes classifier, Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, support vector machine (SVM). Response time of bit-plane GLAC features are considerably lower than HOG and Gabor feature, which makes it an efficient candidate for realtime hand detection systems.