Development and analysis of face recognition system on a mobile robot environment / Jit Shen Quah and Mariam Md Ghazaly

In today's society, face recognition technology is widely used and applied in various fields such as biometric identification and security surveillance. However it is apparent that as technology advances, even more so in the direction of mobile robots such as a mobile security surveillance robo...

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Bibliographic Details
Main Authors: Jit, Shen Quah (Author), Md Ghazaly, Mariam (Author)
Format: Book
Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM), 2018.
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100 1 0 |a Jit, Shen Quah  |e author 
700 1 0 |a Md Ghazaly, Mariam  |e author 
245 0 0 |a Development and analysis of face recognition system on a mobile robot environment / Jit Shen Quah and Mariam Md Ghazaly 
260 |b Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM),   |c 2018. 
500 |a https://ir.uitm.edu.my/id/eprint/36336/1/36336.pdf 
520 |a In today's society, face recognition technology is widely used and applied in various fields such as biometric identification and security surveillance. However it is apparent that as technology advances, even more so in the direction of mobile robots such as a mobile security surveillance robot or a humanoid robot, the application of face recognition would need to transition from the traditional fixed position recognition to a mobile environment recognition as well. This research thus aimed at analyzing the performance of face recognition algorithm performance in a mobile environment as compared to a static environment. This is done via integrating a developed face recognition software onto a mobile robot in terms of image captured distance and in extension its accuracy during static and dynamic conditions. The results from this research shows that when there is an increase in mobile robot speed from 0 ~ 65% duty cycle there seem to be a reduction in performance in terms of range of capture of approximately 30% for both face recognition and face identification which is a clear reduction in performance. From the results as well, the optimum speed for the mobile robot to move to obtain optimum performance for both recognition and identification was found to be at 60% PWM with minimum neighbors and scaling factors both set to 1. 
546 |a en 
690 |a TJ Mechanical engineering and machinery 
655 7 |a Article  |2 local 
655 7 |a PeerReviewed  |2 local 
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787 0 |n https://jmeche.uitm.edu.my/ 
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