Face and Hand Gesture Recognition using Principle Component Analysis and kNN Classifier
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10.5120/17394-7944 |
Adita K Nimbalkar, R R Karhe and C S Patil. Article: Face and Hand Gesture Recognition using Principle Component Analysis and kNN Classifier. International Journal of Computer Applications 99(8):26-28, August 2014. Full text available. BibTeX
@article{key:article,
author = {Adita K Nimbalkar and R. R. Karhe and C. S. Patil},
title = {Article: Face and Hand Gesture Recognition using Principle Component Analysis and kNN Classifier},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {99},
number = {8},
pages = {26-28},
month = {August},
note = {Full text available}
}
Abstract
Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving wavelet transform and principal component analysis for face and hand gesture recognition on digital images.
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