Title: Face recognition under large age gap using age face generation

Authors: Rajesh Kumar Tripathi; Anand Singh Jalal

Addresses: Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India ' Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India

Abstract: Age invariant face recognition (AIFR) is a challenging problem in the area of the face recognition. To handle large age gap for face recognition, we proposed a robust approach based on deep learning for face recognition under a large age gap. The presented approach consists of four important steps. The pre-processing is done for face detection. Age face generation is processed with the help of modified age conditional generative adversarial network (acGAN). Generated age face images are mixed with train dataset and augmentation is applied to increase the size of training data for handling biasness of the deep learning models towards dataset size. A modified residual convolutional neural network is applied for training and testing of face images. The performance has been evaluated using two-fold cross-validation on standard and challenging LAG dataset. The proposed approach achieved the 92.5% recognition accuracy, which is better than the existing face recognition approaches for a large age gap.

Keywords: age invariant; deep learning; generative adversarial network; large age gap; convolutional neural network.

DOI: 10.1504/IJBM.2023.129227

International Journal of Biometrics, 2023 Vol.15 No.2, pp.233 - 250

Received: 06 Jul 2021
Accepted: 03 Feb 2022
Published online: 01 Mar 2023 *

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