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An improved age invariant face recognition using data augmentation

Okokpujie, Kennedy O. and John, S. N. and Ndujiuba, Charles Uzoanya and Badejo, J. A. and Noma-Osaghae, Etinosa (2021) An improved age invariant face recognition using data augmentation. Bulletin of Electrical Engineering and Informatics, 10 (1). pp. 179-191.

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In spite of the significant advancement in face recognition expertise, accurately recognizing the face of the same individual across different ages still remains an open research question. Face aging causes intra-subject variations (such as geometric changes during childhood & adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Over the years, researchers have devised different techniques to improve the accuracy of age invariant face recognition (AIFR) systems. In this paper, the face and gesture recognition network (FG-NET) aging dataset was adopted to enable the benchmarking of experimental results. The FG-Net dataset was augmented by adding four different types of noises at the preprocessing phase in order to improve the trait aging face features extraction and the training model used at the classification stages, thus addressing the problem of few available training aging for face recognition dataset. The developed model was an adaptation of a pre-trained convolution neural network architecture (Inception-ResNet-v2) which is a very robust noise. The proposed model on testing achieved a 99.94% recognition accuracy, a mean square error of 0.0158 and a mean absolute error of 0.0637. The results obtained are significant improvements in comparison with related works.

Item Type: Article
Uncontrolled Keywords: Age invariant face recognition Data augmentation FG-net aging dataset Inception-ResNet-v2 Noise image augmentation
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User: AKINWUMI
Date Deposited: 17 Nov 2023 09:49
Last Modified: 17 Nov 2023 09:49

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