Open AccessComparative analysis of augmented datasets performances of age invariant face recognition models
Author(s) -
Kennedy Okokpujie,
Etinosa NomaOsaghae,
Samuel John,
Charles Uzoanya Ndujiuba,
Imhade P. Okokpujie
Publication year - 2021
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v10i3.3020
Subject(s) - artificial intelligence , convolutional neural network , computer science , pattern recognition (psychology) , preprocessor , facial recognition system , multilayer perceptron , data pre processing , artificial neural network , mean squared error , feature extraction , deep learning , invariant (physics) , face (sociological concept) , mathematics , statistics , social science , sociology , mathematical physics
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%.