Title :
Hand Gesture Recognition Using an Android Device
Author :
Saxena, Ankur ; Jain, D.K. ; Singhal, Achintya
Author_Institution :
Central Electron. Eng. Res. Inst., Pilani, India
Abstract :
In the field of image processing it is very interesting to recognize the human gesture for general life applications. Gesture recognition is a growing field of research among various human computer interactions, hand gesture recognition is very popular for interacting between human and machines. It is nonverbal way of communication and this research area is full of innovative approaches. This paper aims at recognizing 40 basic hand gestures. The main features used are centroid in the hand, presence of thumb and number of peaks in the hand gesture. That is the algorithm is based on shape based features by keeping in mind that shape of human hand is same for all human beings except in some situations. The recognition approach used in this paper is artificial neural network among back propagation algorithm. This approach can be adapted to real time system very easily. In this paper for image acquisition android camera is used, after that frames are send to the server and edge detection of the video is done which is followed by thinning that reduce the noise, tokens are being created from thinning image after tokens are fetched. The paper briefly describes the schemes of capturing the image from android device, image detection, processing the image to recognize the gestures as well as few results.
Keywords :
backpropagation; cameras; edge detection; gesture recognition; image denoising; image thinning; neural nets; real-time systems; shape recognition; smart phones; video signal processing; Android camera; Android device; artificial neural network; back propagation algorithm; edge detection; general life applications; hand centroid; hand gesture recognition; human computer interactions; human gesture recognition; human hand shape; human machines interaction; image acquisition; image detection; image noise reduction; image processing; image thinning; innovative approaches; nonverbal communication; real time system; shape based features; video; Androids; Biological neural networks; Gesture recognition; Humanoid robots; Image edge detection; Neurons; Android; Edge Detection; Sobel algorithm; gesture recognition; neural network; token detection;
Conference_Titel :
Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on
Conference_Location :
Bhopal
Print_ISBN :
978-1-4799-3069-2
DOI :
10.1109/CSNT.2014.170