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Appearance-Based Feature Extraction Techniques for Facial Recognition: Comparative Study

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dc.contributor.author Akintola, Abimbola Ganiyat
dc.contributor.author Aro, Taye Oladele
dc.contributor.author Oniyangi, Abdul-hafiz Taiwo
dc.date.accessioned 2020-06-09T04:55:03Z
dc.date.available 2020-06-09T04:55:03Z
dc.date.issued 2020-01
dc.identifier.issn 1818-5878
dc.identifier.issn 2408-8498
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/3942
dc.description.abstract One of the important steps that must be considered in developing a robust facial recognition is feature extraction. The rate of recognition in the facebased biometric system can be determined by the amount of measurable and relevant features extracted from the face image. Several feature extraction algorithms in appearance-based technique such as Linear Discriminant Analysis (LDA), Independent Analysis (LDA) and Principal Component Analysis (PCA) have been used in face recognition. This paper applied Contrast Limited Adaptive Histogram Equalization (CLAHE) before three appearancebased feature extraction algorithms: PCA, LDA and combined PCA/LDA for face recognition system. A comparative analysis was conducted on the three techniques, experimental results showed that the PCA technique recorded the best recognition accuracy (RA) of 95.65% for ORL database, the best False Rejection Rate (FRR) of 0.1250 in LDA for FERET database and the best False Acceptance Rate (FAR) of 0.5000 in PCA / LDA for FERET database. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Human face recognition en_US
dc.subject Face perception en_US
dc.subject Image processing en_US
dc.title Appearance-Based Feature Extraction Techniques for Facial Recognition: Comparative Study en_US
dc.type Article en_US

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