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A Bimodal Biometric Student Attendance System

Atuegwu , Charity and Okokpujie, Kennedy O. and Noma-Osaghae , Etinosa (2017) A Bimodal Biometric Student Attendance System. In: 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON) , 7-10 Nov - 2017, Federal University of Technology, Owerri, Nigeria. . (In Press)

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Abstract

A lot of attempts have been made to use biometrics in class attendance systems. Most of the implemented biometric attendance systems are unimodal. Unimodal biometric systems may be spoofed easily, leading to a reduction in recognition accuracy. This paper explores the use of bimodal biometrics to improve the recognition accuracy of automated student attendance systems. The system uses the face and fingerprint to take students’ attendance. The students’ faces were captured using webcam and preprocessed by converting the color images to grey scale images. The grey scale images were then normalized to reduce noise. Principal Component Analysis (PCA) algorithm was used for facial feature extraction while Support Vector Machine (SVM) was used for classification. Fingerprints were captured using a fingerprint reader. A thinning algorithm digitized and extracted the minutiae from the scanned fingerprints. The logical technique (OR) was used to fuse the two biometric data at the decision level. The fingerprint templates and facial images of each user were stored along with their particulars in a database. The implemented system had a minimum recognition accuracy of 87.83%.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User: Engr. Kennedy Okokpujie
Date Deposited: 30 Dec 2017 15:06
Last Modified: 30 Dec 2017 15:06
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/9870

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