Static Hand Gesture Recognition System Using Artificial Neural Networks and Support Vector Machine

Julius BAMWENDA; Mehmet Siraç ÖZERDEM

doi:https://doi.org/10.24012/dumf.569357

Makale detayları

Static Hand Gesture Recognition System Using Artificial Neural Networks and Support Vector Machine

Yazarlar:

Julius BAMWENDA | Mehmet Siraç ÖZERDEM

Yayıncı:

Dicle University

Ana Başlıklar:

Mühendislik ve uygulamalı operasyonlar

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 2019 Vol.10 Issue 2, pp.561-568

Belge Tipi:

Makale

Yayın Tarihi:

6/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000950268

Sıklık:

Üç Aylık

eISSN:

2146-4391

pISSN:

1309-8640

DOI:

https://doi.org/10.24012/dumf.569357

Anahtar kelimeler:

Özet:

 


Hand gesture recognition is a relevant study topic for a reason that sometimes we may not be in position to communicate verbally. There is need to design Hand gesture recognition systems in order to help people adopt to nonverbal communication mainly sign language. However, there is no clue to understand the meaning of gesture through the computers directly. So this calls for definitions that generalize models in a computer. That is why the machine-learning approaches are implemented in recognition systems. There are generally two types of hand gestures recognition systems which researches have concentrated on. These include static and dynamic Hand gesture recognition systems. However, in building Hand gesture recognition systems, various machine learning approaches have been used. For implementing the proposed system, MS Kinect depth sensor was used as a hardware. The Kinect depth sensor is composed of an infrared camera. This is an advantage to the systems that are designed basing on the depth sensing because factors like color, clothing and background have less effect on the performance. So Kinect based depth sensor systems have a high accuracy and performance making them relevant and applicable in our daily lives. In this paper, we propose a static hand gesture recognition system in real time using two machine learning methods namely Support Vector Machine and Artificial Neural Networks. We use of the newly launched Microsoft Kinect sensor for image extraction. The sensor helps us to extract the hand images. We implement the system on a Matlab platform for reasons that Matlab is widely used by researchers in different fields and that can handle complex computations. In the training of the model, we collect a hundred depth-based Histogram of Oriented Gradient features per alphabet from the hand gesture images which we trained, tested and validated using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). From this dataset, we can generate the generalized gesture model for each alphabet image. For the proposed system, the classification with ANN proves a higher performance then SVM.


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Makale detayları

Static Hand Gesture Recognition System Using Artificial Neural Networks and Support Vector Machine

Yazarlar:

Julius BAMWENDA | Mehmet Siraç ÖZERDEM

Yayıncı:

Dicle University

Ana Başlıklar:

Mühendislik ve uygulamalı operasyonlar

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 2019 Vol.10 Issue 2, pp.561-568

Belge Tipi:

Makale

Yayın Tarihi:

6/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000950268

Sıklık:

Üç Aylık

eISSN:

2146-4391

pISSN:

1309-8640

DOI:

https://doi.org/10.24012/dumf.569357

Dynamic / Static Hand Gesture Recognition

Artificial Neural Network

Histogram of Oriented Gradient

Support Vector Machine

Sayı

Makale detayları

Static Hand Gesture Recognition System Using Artificial Neural Networks and Support Vector Machine

Yazarlar: Julius BAMWENDA | Mehmet Siraç ÖZERDEM

Yayıncı: Dicle University

Ana Başlıklar: Mühendislik ve uygulamalı operasyonlar

Dergi: Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 2019 Vol.10 Issue 2, pp.561-568

Belge Tipi: Makale

Yayın Tarihi: 6/2019

Belge Dili: İngilizce

Document ID (Org No.): 2000950268

Sıklık: Üç Aylık

eISSN: 2146-4391

pISSN: 1309-8640

DOI: https://doi.org/10.24012/dumf.569357

Dynamic / Static Hand Gesture Recognition

Artificial Neural Network

Histogram of Oriented Gradient

Support Vector Machine

Sayı

Yazarlar:

Julius BAMWENDA | Mehmet Siraç ÖZERDEM

Yayıncı:

Dicle University

Ana Başlıklar:

Mühendislik ve uygulamalı operasyonlar

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 2019 Vol.10 Issue 2, pp.561-568

Belge Tipi:

Makale

Yayın Tarihi:

6/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000950268

Sıklık:

Üç Aylık

eISSN:

2146-4391

pISSN:

1309-8640

DOI:

https://doi.org/10.24012/dumf.569357