Hand gesture recognition using discrete wavelet transform and convolutional neural network

Muhammad Biyan Priatama, Ledya Novamizanti, Suci Aulia, Erizka Banuwati Candrasari

Abstract


Public services are available to all communities including people with disabilities. One obstacle that impedes persons with disabilities from participating in various community activities and enjoying the various public services available to the community is information and communication barriers. One way to communicate with people with disabilities is with hand gestures. Therefore, the hand gesture technology is needed, in order to facilitate the public to interact with the disability. This study proposes a reliable hand gesture recognition system using the convolutional neural network method. The first step, carried out pre-processing, to separate the foreground and background. Then the foreground is transformed using the discrete wavelet transform (DWT) to take the most significant subband. The last step is image classification with convolutional neural network. The amount of training and test data used are 400 and 100 images repectively, containing five classes namely class A, B, C, # 5, and pointing. This study engendered a hand gesture recognition system that had an accuracy of 100% for dataset A and 90% for dataset B.


Keywords


Convolutional neural networks; Discrete wavelet transform; Hand gesture recognition

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DOI: https://doi.org/10.11591/eei.v9i3.1977

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