Improving Periocular Recognition Accuracy: Opposite Side Learning Suppression and Vertical Image Inversion

Masakazu Fujio, Yosuke Kaga, Kenta Takahashi

2025

Abstract

Periocular recognition has emerged as an effective biometric identification method in recent years, particularly when the face is partially occluded, or the iris image is unavailable. This paper proposes a deep learning-based periocular recognition method specifically designed to address the overlooked issue of simultaneously training left and right periocular images from the same person. Our proposed method enhances recognition accuracy by identifying the eye side, applying a vertical flip during training and inference, and stopping backpropagation for the opposite side of the current periocular. Experimental results on visible and NIR image datasets, using six different off-the-shelf deep CNN models, demonstrate an approximate 1∼2% improvement in recognition accuracies compared to conventional approaches that employ horizontal flip to align the appearance of the right and left eyes. The proposed approach’s performance was compared with state-of-the-art methods in the literature on three unconstrained periocular datasets: CASIA-Iris-Distance, UBIPr. The experimental results indicated that our approach consistently outperformed the state-of-the-art methods on these datasets. From the perspective of implementation costs, the proposed method is applied during training and does not affect the computational complexity during inference. Moreover, during training, the method only sets the gradient values of the periocular image class of the opposite side to zero, thus having a minimal impact on the computational cost. It can be combined easily with other periocular authentication methods.

Paper Citation

in Harvard Style

Fujio M., Kaga Y. and Takahashi K. (2025). Improving Periocular Recognition Accuracy: Opposite Side Learning Suppression and Vertical Image Inversion. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 298-305. DOI: 10.5220/0013123400003912

in Bibtex Style

@conference{visapp25,
author={Masakazu Fujio and Yosuke Kaga and Kenta Takahashi},
title={Improving Periocular Recognition Accuracy: Opposite Side Learning Suppression and Vertical Image Inversion},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={298-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013123400003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Improving Periocular Recognition Accuracy: Opposite Side Learning Suppression and Vertical Image Inversion
SN - 978-989-758-728-3
AU - Fujio M.
AU - Kaga Y.
AU - Takahashi K.
PY - 2025
SP - 298
EP - 305
DO - 10.5220/0013123400003912
PB - SciTePress