Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification
The study presented aims to design and develop a face recognition system. The system utilized Viola Jones Algorithm in detecting faces from a given image. Also the system used Artificial Neural Networks in recognizing faces detected from the input. Upon experimentation the system generated can recog...
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oai:animorepository.dlsu.edu.ph:faculty_research-29332021-08-02T02:10:37Z Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification Fernandez, Ma Christina D. Gob, Kristina Joyce E. Leonidas, Aubrey Rose M. Ravara, Ron Jason J. Bandala, Argel A. Dadios, Elmer P. The study presented aims to design and develop a face recognition system. The system utilized Viola Jones Algorithm in detecting faces from a given image. Also the system used Artificial Neural Networks in recognizing faces detected from the input. Upon experimentation the system generated can recognize human faces with accuracy of 87.05%. The system performs at its best if the person is around 150cm away from the camera with an accuracy rate of 87.59%. Also, the best amount of lighting for the recognition system is at 480 lumens with an accuracy rate of 88.64%. Lastly, the system also performs at its best if the person is directly facing the camera or at 0 degrees with respect to the camera. © 2014 IEEE. 2014-07-23T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/1934 Faculty Research Work Animo Repository Human face recognition (Computer science) Image processing—Digital techniques Neural networks (Computer science) Electrical and Computer Engineering |
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Human face recognition (Computer science) Image processing—Digital techniques Neural networks (Computer science) Electrical and Computer Engineering Fernandez, Ma Christina D. Gob, Kristina Joyce E. Leonidas, Aubrey Rose M. Ravara, Ron Jason J. Bandala, Argel A. Dadios, Elmer P. Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification |
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The study presented aims to design and develop a face recognition system. The system utilized Viola Jones Algorithm in detecting faces from a given image. Also the system used Artificial Neural Networks in recognizing faces detected from the input. Upon experimentation the system generated can recognize human faces with accuracy of 87.05%. The system performs at its best if the person is around 150cm away from the camera with an accuracy rate of 87.59%. Also, the best amount of lighting for the recognition system is at 480 lumens with an accuracy rate of 88.64%. Lastly, the system also performs at its best if the person is directly facing the camera or at 0 degrees with respect to the camera. © 2014 IEEE. |
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text |
author |
Fernandez, Ma Christina D. Gob, Kristina Joyce E. Leonidas, Aubrey Rose M. Ravara, Ron Jason J. Bandala, Argel A. Dadios, Elmer P. |
author_facet |
Fernandez, Ma Christina D. Gob, Kristina Joyce E. Leonidas, Aubrey Rose M. Ravara, Ron Jason J. Bandala, Argel A. Dadios, Elmer P. |
author_sort |
Fernandez, Ma Christina D. |
title |
Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification |
title_short |
Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification |
title_full |
Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification |
title_fullStr |
Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification |
title_full_unstemmed |
Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification |
title_sort |
simultaneous face detection and recognition using viola-jones algorithm and artificial neural networks for identity verification |
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Animo Repository |
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2014 |
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https://animorepository.dlsu.edu.ph/faculty_research/1934 |
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1707059243806359552 |