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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Kennedy Okokpujie
Covenant University
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Comparative analysis of augmented datasets performances of age invariant face recognition models Kennedy Okokpujie; Etinosa Noma-Osaghae; Samuel Ndueso John; Charles Ndujiuba; Imhade Princess Okokpujie
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3020

Abstract

The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%.
An improved age invariant face recognition using data augmentation Kennedy Okokpujie; Samuel John; Charles Ndujiuba; Joke A. Badejo; Etinosa Noma- Osaghae
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2356

Abstract

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 and 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.
Co-Authors Adewale Adeyinka Amaize Peter Charles Ndujiuba Charles Ndujiuba Etinosa Noma Osaghae Etinosa Noma- Osaghae Etinosa Noma-Osaghae Ibinabo Bob-Manuel Ighalo Joshua Imhade P. Okokpujie Imhade Princess Okokpujie John Abubakar Joke A. Badejo Nsikan Nkordeh Obinna Okoyeigbo Oboyerulu Agboje Ochonogor Charles Okokpujie Imhade Olamilekan Shobayo Olatunji Okesola Ololade Oladoyin Omoruyi Osemwegie Samuel John Samuel John Samuel John Samuel Ndueso John Uzairue Stanley Uzairue Stanley Idiake