Title :
Histograms of orientation gradient investigation for static hand gestures
Author :
Sheenu ; Joshi, Garima ; Vig, Renu
Author_Institution :
ECE Dept., Panjab Univ., Chandigarh, India
Abstract :
In this paper, Histograms of Orientation Gradient (HOG) algorithm is used to identify the static hand gestures. Experimental results show that HOG descriptor is a better shape descriptor than existing feature sets for gesture recognition. The overall algorithm has only three main steps; pre-processing, feature extraction and classification. It completely omits the segmentation phase. SVM is used for recognition of gestures. High recognition accuracy is achieved for 11 hand gestures.
Keywords :
feature extraction; gesture recognition; image classification; support vector machines; HOG algorithm; HOG descriptor; SVM; classification; feature extraction; gesture recognition; histogram of orientation gradient investigation; preprocessing; shape descriptor; static hand gestures; Accuracy; Feature extraction; Gesture recognition; Histograms; Image color analysis; Shape; Support vector machines; Histograms of Oriention Gradient; feature extraction; hand gesture; human computer interaction;
Conference_Titel :
Computing, Communication & Automation (ICCCA), 2015 International Conference on
Conference_Location :
Noida
Print_ISBN :
978-1-4799-8889-1
DOI :
10.1109/CCAA.2015.7148539
