Gesture recognition system for Nigerian tribal greeting postures using support vector machine / Segun Aina …[et al.]

Aina, Segun and V. Sholesi, Kofoworola and R. Lawal, Aderonke and D. Okegbile, Samuel and I. Oluwaranti, Adeniran (2020) Gesture recognition system for Nigerian tribal greeting postures using support vector machine / Segun Aina …[et al.]. Malaysian Journal of Computing (MJoC), 5 (2). pp. 609-618. ISSN (eISSN): 2600-8238

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Abstract

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

Metadata

Item Type: Article
Creators:
Creators
Email
Aina, Segun
s.aina@oauife.edu.ng
V. Sholesi, Kofoworola
sholesikofoworola@gmail.com
R. Lawal, Aderonke
alawal@oauife.edu.ng
D. Okegbile, Samuel
sokegbile@oauife.edu.ng
I. Oluwaranti, Adeniran
niranoluwaranti@oauife.edu.ng
Subjects: Q Science > Q Science (General) > Cybernetics
Q Science > QA Mathematics > Analysis
Q Science > QA Mathematics > Analysis > Calculus
T Technology > TA Engineering. Civil engineering > Applied optics. Photonics > Optical pattern recognition
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Malaysian Journal of Computing (MJoC)
UiTM Journal Collections: UiTM Journal > Malaysian Journal of Computing (MJoC)
ISSN: (eISSN): 2600-8238
Volume: 5
Number: 2
Page Range: pp. 609-618
Official URL: https://mjoc.uitm.edu.my
Item ID: 48129
Uncontrolled Keywords: Gaussian Blur, Greeting, SVM
URI: https://ir.uitm.edu.my/id/eprint/48129

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48129

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