HAND GESTURE PHASE CLASSIFICATION USING MULTILAYER PERCEPTRON

Authors

  • Ashutosh Mohite Department of I.T., Dr. C. V. Raman University, Kota, Bilaspur, Chhattisgarh, India
  • Dr. S. M. Ghosh Department of I.T., Dr. C. V. Raman University, Kota, Bilaspur, Chhattisgarh, India

Keywords:

Multilayer Perceptron, Hand Gesture, Human-Computer Interaction (HCI), Non Verbal Communication

Abstract

Symbolic and Sign language is a most important technique for the non-verbal communication which uses the gestures i.e. the movement of the body part which carry some information to perform the desired goal. Gesture is a meaningful statements or information given by the human beings to accomplish the specific task. This paper presents an approach for Human-Computer Interaction (HCI) where we classify the different hand gesture phases using the movement of the hand as the input device. The main objective of this paper is to make a robust model to identify the different hand gesture phases using Multilayer Perceptron (MLP) with high accuracy.The MLP gives better classification accuracy as 83.95% with learning rate 0.6 and hidden layer 3.

References

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Additional Files

Published

15-05-2017

How to Cite

Ashutosh Mohite, & Dr. S. M. Ghosh. (2017). HAND GESTURE PHASE CLASSIFICATION USING MULTILAYER PERCEPTRON. International Education and Research Journal (IERJ), 3(5). Retrieved from http://ierj.in/journal/index.php/ierj/article/view/819