Paper Info
Title
A novel hand gesture recognition method using Principal Directional Features
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.
Year
DOI
Venue
2013
10.1109/ROBIO.2013.6739638
Robotics and Biomimetics
Keywords
Field
DocType
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
Computer vision,Longest common subsequence problem,Pattern recognition,Segmentation,Computer science,Gesture,Gesture recognition,Feature extraction,Image segmentation,Artificial intelligence,Contextual image classification,Classifier (linguistics)
Conference
Citations 
PageRank 
References 
0
0.34
3
Authors
3
Name
Order
Citations
PageRank
Mahmood Jasim142.37
Tao Zhang2422100.57
M. Hasanuzzaman3132.63