Aworinde Halleluyah Oluwatobi

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



Research Interests: Image Processing, Computational Learning Theory


Aworinde, Halleluyah Oluwatobi lectures at the Department of Computer Science & Information Technology at Bowen University, Iwo, Nigeria. He had his B.Tech and M.Sc. in Computer Science from Ladoke Akintola University of Technology Ogbomoso and University of Ibadan, Ibadan, Nigeria respectively. He is currently on his Doctoral programme and his research interest includes Computational Intelligence with deep bias for Deep Learning and Image Processing. He has to his credit a number of publications in learned Journals and Conference Proceedings. He is a member of IACSIT and IAENG.

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

By Aworinde Halleluyah Oluwatobi Onifade O.F.W.

DOI:, 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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