Title :
A novel hand gesture recognition method using Principal Directional Features
Author :
Jasim, Mahmood ; Tao Zhang ; Hasanuzzaman, Md
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Dhaka, Dhaka, Bangladesh
Abstract :
This paper presents a novel hand gesture recognition method based on Principal Directional Features (PDF). The image sequence is captured using a fixed mounted monocular camera to recognize dynamic gestures. Haar-like feature based cascaded classifier is used for hand area segmentation. Text based Principal Directional Features are extracted from the segmented images. Longest Common Subsequence algorithm is used to recognize the gestures from text based PDF. The Directional Gesture dataset is prepared containing complex dynamic gestures to test this system and achieved 94% accuracy in recognizing dynamic hand gestures.
Keywords :
Haar transforms; feature extraction; gesture recognition; image classification; image segmentation; image sequences; Haar-like feature based cascaded classifier; directional gesture dataset; dynamic gesture recognition; dynamic hand gesture recognition; fixed mounted monocular camera; hand area segmentation; hand gesture recognition method; image sequence; longest common subsequence algorithm; principal directional features; text based PDF; text based principal directional feature extraction; Accuracy; Equations; Feature extraction; Gesture recognition; Image segmentation; Image sequences; Mathematical model;
Conference_Titel :
Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on
Conference_Location :
Shenzhen
DOI :
10.1109/ROBIO.2013.6739638
