Face recognition using PZMI, ANN and Ant colony algorithms / Milad Miri
Face recognition system is part of facial image processing applications, which is one of the biometric methods to identify people by the features of the face. This system has many usages in security system and also can be used for authentication, person verification, video surveillance, preventing c...
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| Format: | Thesis |
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2018
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| Online Access: | http://studentsrepo.um.edu.my/11331/2/Milad_Miri.pdf http://studentsrepo.um.edu.my/11331/1/Milad_Miri.pdf http://studentsrepo.um.edu.my/11331/ |
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| Summary: | Face recognition system is part of facial image processing applications, which is one of the biometric methods to identify people by the features of the face. This system has many usages in security system and also can be used for authentication, person verification, video surveillance, preventing crime, and security activities. Usually, most of the standard face recognition systems contain four sections: face detection, feature extraction, feature selection, and classification. Although there are many barriers for each part of this system, many algorithms are also created to tackle these limitations. Algorithms developed for face recognition are tightly related to the rate of extracted face features. The huge redundant number of extracted features can reduce the performance of face recognition system drastically and increase the time to complete the whole process surprisingly. So, it is important to choose a proper combination of algorithms that not only diminishes the number of selected features which reduce the executing time of the system, but also improves the rate of efficiency and performance of face recognition. This study applies a new set of combination, which is Discrete Wavelet Transform (DWT) and Pseudo Zernike Moment Invariant (PZMI) for feature extraction with Ant Colony Optimization (ACO) in collaboration with Artificial Neural Network (ANN) that is experimented for the first time in the face recognition domain. ORL database has been employed as the primary dataset. The accuracy rate resulted from the system is 88.25% for PZMI+ACO+ANN and 81.34% for DWT+ACO+ANN. This research provides a new opportunity for researchers to develop face recognition system further. Researchers should be aware that the real-world conditions can be different and unpredictable as compared to the lab conditions. Online face recognition system has limitations which can motivate them to investigate more rigorously in this area.
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