Joseph Damilola Akinyemi

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



Research Interests: Computer systems and computational processes, Computer Vision, Pattern Recognition, Computer Architecture and Organization, Data Structures and Algorithms


Joseph Damilola Akinyemi is a lecturer and researcher at the Department of Computer Science, University of Ibadan, Ibadan, Nigeria. He obtained his B.Sc. in Computer Science from the University of Ilorin, Ilorin, Nigeria and obtained both his Masters and Doctorate degrees from the University of Ibadan, Ibadan, Nigeria. His research interest is in Computer Vision, Pattern Recognition and Machine Learning.

Author Articles
An Individualized Face Pairing Model for Age-Invariant Face Recognition

By Joseph Damilola Akinyemi Olufade F. W. Onifade

DOI:, Pub. Date: 8 Feb. 2023

Among other factors affecting face recognition and verification, the aging of individuals is a particularly challenging one. Unlike other factors such as pose, expression, and illumination, aging is uncontrollable, personalized, and takes place throughout human life. Thus, while the effects of factors such as head pose, illumination, and facial expression on face recognition can be minimized by using images from controlled environments, the effect of aging cannot be so controlled. This work exploits the personalized nature of aging to reduce the effect of aging on face recognition so that an individual can be correctly recognized across his/her different age-separated face images. To achieve this, an individualized face pairing method was developed in this work to pair faces against entire sets of faces grouped by individuals then, similarity score vectors are obtained for both matching and non-matching image-individual pairs, and the vectors are then used for age-invariant face recognition. This model has the advantage of being able to capture all possible face matchings (intra-class and inter-class) within a face dataset without having to compute all possible image-to-image pairs. This reduces the computational demand of the model without compromising the impact of the ageing factor on the identity of the human face. The developed model was evaluated on the publicly available FG-NET dataset, two subsets of the CACD dataset, and a locally obtained FAGE dataset using leave-one-person (LOPO) cross-validation. The model achieved recognition accuracies of 97.01%, 99.89%, 99.92%, and 99.53% respectively. The developed model can be used to improve face recognition models by making them robust to age-variations in individuals in the dataset.

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