Large Age Gap Face Verification by Learning GAN Synthesized Prototype Representations

Swastik Jena, Bunil Balabantaray, Rajashree Nayak

2024

Abstract

A phenomenal growth in the field of face recognition has been witnessed over the last few years. Existing deep learning-based face recognition methodologies employ auxiliary age classifiers and intermediate age synthesizers to address the discrepancies in facial appearance due to aging. However, even after training on large amount of annotated data samples and by utilizing prior information the existing methodologies still underperform in recognizing the large intra-class age variance posed by images of same identity. LAG is a challenging face verification benchmark dataset having very few samples per identity with large age variance and no age annotations. This paper aims to perform face verification on the LAG dataset by learning the large intra-class variance posed by aging. The proposed work integrates a new training regime for the face verification task. SimSwap GAN is used for generating hybrid faces from young and adult images present in the LAG dataset. A Prototype Feature Activation (PFA) network is used to extract the feature embeddings of the hybrid faces and a modified Siamese Neural Network is trained to learn the face embeddings combined with attention-enhanced feature fusion. Extensive experiments highlight the outperforming performance of the proposed approach compared with existing baseline face verification methods on the LAG dataset.

Paper Citation

in Harvard Style

Jena S., Balabantaray B. and Nayak R. (2024). Large Age Gap Face Verification by Learning GAN Synthesized Prototype Representations. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 70-78. DOI: 10.5220/0012398800003654

in Bibtex Style

@conference{icpram24,
author={Swastik Jena and Bunil Balabantaray and Rajashree Nayak},
title={Large Age Gap Face Verification by Learning GAN Synthesized Prototype Representations},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={70-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012398800003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Large Age Gap Face Verification by Learning GAN Synthesized Prototype Representations
SN - 978-989-758-684-2
AU - Jena S.
AU - Balabantaray B.
AU - Nayak R.
PY - 2024
SP - 70
EP - 78
DO - 10.5220/0012398800003654
PB - SciTePress