Hand Sign Recognition using Infrared Imagery Provided by Leap Motion Controller and Computer Vision
Tathagat Banerjee, K. V. Pavan Srikar, S. Ashvith Reddy, Krishna Sai Biradar, Rithika Reddy Koripally, Gummadi. Varshith
Abstract
Speech Impairment and conversion of sign language to human re-engineered audio signals is something computer science has always been interested in. However, the architectural robustness and extraction of features on a very insignificant area of change have posed decade long problems to achieve this idea. The paper proposes a Convolutional Neural network based on a deep belief model on Data imagery collected by leap motion controllers on hand sign recognition. The database is composed of 10 different hand-gestures that were performed by 10 different subjects (5 men and 5 women) which is presented, composed by a set of near-infrared images acquired by the Leap Motion sensor. The paper tries to achieve high accuracy on the pertaining training set inorder to create and form a robust model. It embraces the first step towards image understanding of human signs and aid specially-abled people. We have implemented and tested the algorithm for 2000 images each class. The paper achieves the accuracy and precision of 99.4% and 99.68% respectively. The implications of the study intend to enhance understanding of infrared imagery for small areas of localization feature detection and intend to help the idea of human audio re-engineering a resurgence by using the same.