Title: Deep learning-driven multi-sample periocular recognition for biometric authentication

Authors: Nabil Hezil; Amir Benzaoui; Ghania Droua-Hamdani; Khadidja Belattar; Ahmed Bouridane

Addresses: Scientific and Technical Research Center for the Development of Arabic Language (CRSTDLA), Algiers, Algeria; Computer Engineering Department, University of Sharjah, Sharjah, UAE ' Electrical Engineering Department, University of Skikda, Skikda, Algeria ' Scientific and Technical Research Center for the Development of Arabic Language (CRSTDLA), Algiers, Algeria ' Faculty of Sciences, University of Algiers, Algiers, Algeria ' Computer Engineering Department, University of Sharjah, Sharjah, UAE

Abstract: The COVID-19 pandemic has accelerated the adoption of contactless biometric modalities such as face, iris, voice, and periocular recognition, which offer a safer alternative to traditional methods by reducing the risk of disease transmission in public and private spaces. While face recognition technologies have shown robust performance even with partial facial occlusions, their accuracy significantly diminishes when individuals wear medical masks, highlighting the importance of periocular biometrics for reliable personal identification. To enhance security and accuracy, multi-biometric systems - combining multiple biometric traits - outperform single-modality approaches. We propose a multi-input convolutional neural network (MICNN) framework that fuses the left and right periocular traits from the same face image for enhanced biometric recognition. We evaluate our method on two challenging periocular datasets, achieving highly competitive correct recognition rates of 99.62% and 98.33%, respectively, outperforming recent benchmarks. These results underscore the efficacy of multi-sample periocular recognition using deep learning for contactless biometric identification.

Keywords: multi-sample biometrics; periocular recognition; fusion; deep learning; cascade object detector; multi-input convolutional neural network; MICNN; feed-forward neural networks; FFNNs.

DOI: 10.1504/IJBM.2026.154563

International Journal of Biometrics, 2026 Vol.18 No.4, pp.362 - 386

Received: 30 Sep 2024
Accepted: 07 Jun 2025
Published online: 06 Jul 2026 *

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