Onifade O.F.W.

Work place: Department of Computer Science, University of Ibadan, Ibadan, Nigeria

E-mail: olufadeo@gmail.com


Research Interests: Pattern Recognition, Information Retrieval


Olufade F.W Onifade obtained a PhD in Computer Science from Nancy 2 University, Nancy, France in 2009. He is currently a Senior Lecturer at the Computer Science Department, University of Ibadan, Ibadan, Nigeria. He has published over 70 papers in both local and International referred journals and conferences and has held several fellowships including ETTMIT and the CV Raman Fellowship for African Researchers in India. His research interests include Fuzzy Learning, Information Retrieval, Biometrics and Pattern Matching. Dr. Onifade is a member of IEEE, IAENG and CPN.

Author Articles
A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification

By Aworinde Halleluyah Oluwatobi Onifade O.F.W.

DOI: https://doi.org/10.5815/ijem.2019.02.05, Pub. Date: 8 Mar. 2019

There is paucity of information on the possibility of ethnicity identification through fingerprint biometric characteristics and so, this work is set to combine two soft biometric traits (Gender and Ethnicity) in order to ascertain if individual of different ethnicity and gender bias can be identified through their fingerprint. Live scan mechanism was used in order to minimize human errors and as well speed up the rate of fingerprint acquisition which unequivocally ensure good quality capturing of the fingerprint image.
In this work, fingerprints of over a thousand people from three different ethnic groups of both male and female gender in Nigeria were captured and subjected to training, testing and classification using Gabor filter and K-NN respectively. Histogram equalization was used for image enhancement and the system performance was evaluated on the basis of some selected metrics such as Recognition Accuracy, Average Recognition Time, Specificity and Sensitivity. Result of this work indicated over 96% accuracy in predicting person’s ethnicity and gender with an average recognition time of less than 2secs.

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