scholarly journals Hand gesture recognition using discrete wavelet transform and hidden Markov models

2020 ◽  
Vol 18 (5) ◽  
pp. 2265
Author(s):  
Erizka Banuwati Candrasari ◽  
Ledya Novamizanti ◽  
Suci Aulia
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Hidden Markov Models ◽  
Gesture Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Discrete Wavelet

scholarly journals Discrete Wavelet Transform on static hand gesture recognition

2019 ◽  
Vol 1367 ◽  
pp. 012022
Author(s):  
Erizka Banuwati Candrasari ◽  
Ledya Novamizanti ◽  
Suci Aulia
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Discrete Wavelet

scholarly journals Hand gesture recognition using discrete wavelet transform and convolutional neural network

2020 ◽  
Vol 9 (3) ◽  
pp. 996-1004 ◽  
Author(s):  
Muhammad Biyan Priatama ◽  
Ledya Novamizanti ◽  
Suci Aulia ◽  
Erizka Banuwati Candrasari
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Discrete Wavelet Transform ◽  
Gesture Recognition ◽  
Public Services ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Discrete Wavelet

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.

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