Open AccessExperimenting Hand-Gesture Image Recognition using Simple Deep Neural Network
Author(s) -
Mostafa Alghamdi,
Tami Alwajeeh,
Fahad Aljabeer,
Setiawan Assegaff,
Rahmat Budiarto
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.32.18403
Subject(s) - gesture , computer science , gesture recognition , artificial intelligence , artificial neural network , computer vision , deep learning , image (mathematics) , simple (philosophy) , layer (electronics) , pattern recognition (psychology) , interactivity , speech recognition , multimedia , philosophy , chemistry , organic chemistry , epistemology
Traditionally human interacts with a computer by using keyboard and mouse. Considering person with handicapped from the wrist to the fingertip or amputated wrists or fingertips need alternative way; using voice or hand gesture. This work focuses on the use of hand-gesture image recognition. There are two main issues should be considered; less interactivity in static hand gesture recognition, and less accuracy in dynamic hand gesture recognition. This paper attempts to improve the accuracy of hand-gesture image recognition by experimenting simple deep learning neural network (DLNN). As this work uses a simple DLNN, the relation between the hidden layers is not considered. The number of hidden layers in the proposed architecture of the DLNN for the experiments vary from one to five.With the aims to understand the effect of the number of neurons in the hidden layers, the DLNN is experimented using different numbers of hidden neurons. Six different types of hand gestures are considered. 800 videos on hand gestures taken from Vision for Intelligent Vehicles and Applications (VIVA) portal are used in the experiment. The data is divided into two; one as training data and another part is for testing. The best result is achieved when the DLNN uses two hidden layers with 250 neurons in the first hidden layer, and 100 neurons in the second hidden layer. The average of the achieved accuracy level is 77.56%. Experimental results also show that the more number of hidden layer causes over-fitting (does not make the recognition better). It is also observed that the increase of hidden layer number and hidden neurons only affect the accuracy of recognition of the trained dataset and does not improve the recognition of untrained dataset. This result is because the interrelation among the hidden layer are not considered.