Study on hand gesture recognition with CNN-based deep learning Online publication date: Thu, 28-Oct-2021
by Buemjun Kim; Kyounghee Lee
International Journal of Computational Vision and Robotics (IJCVR), Vol. 11, No. 6, 2021
Abstract: Natural user interface technology is actively studied to enable a computer to understand a human's natural behaviours including gestures and expressions. To recognise human motions, while most approaches generally require special devices such as infrared cameras or motion sensors, we propose a system based on deep learning to recognise a user's hand gestures in general images. A key feature of our system is pre-processing of input images to improve training efficiency and inference accuracy of a deep neural network model to classify hand gesture images. It performs black-white binarisation of each image to effectively distinguish a hand area before training a deep neural network. Our implementation shows the proposed system has a practicality for correctly classifying various hand gesture images such as decimal number 0~9 and Korean alphabet consonants. It is also shown that the confidence degree of those classifications can be considerably improved by the proposed image pre-processing.
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