Toward more efficient iris recognition using a lightweight CNN framework with attention mechanism

Qinhong Zou; Yubin Sun; Jiongming Qin; Zeqiong Huang; Bin Chen

doi:10.1117/12.2640778

3 October 2022 Toward more efficient iris recognition using a lightweight CNN framework with attention mechanism

Qinhong Zou, Yubin Sun, Jiongming Qin, Zeqiong Huang, Bin Chen

Proceedings Volume 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022); 122900F (2022) https://doi.org/10.1117/12.2640778
Event: International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022, Zhuhai, China

Abstract

Iris recognition is considered as one of the most promising biometrics due to its discriminative features and friendly acquisition methods. Herein, a deep learning-based method is proposed to achieve more accurate and efficient iris recognition. The proposed framework Iris Attention Network (IrisAttenNet) integrates the attention mechanism into a lightweight CNN to extract iris features more specifically. In the process of feature learning, the channel features with more information that contribute to the recognition result will attract more attention and be given higher weights, which is similar to the human visual perception mechanism. The performance of the proposed framework is evaluated by four publicly available datasets representing different intra-class variations: CASIA_Iris_V4 Interval, Lamp, Thousand and UBIRIS.v1. The experimental results have demonstrated that the approach based on the IrisAttenNet shows higher accuracy, stronger generalization and less computational cost. The intermediate outcomes heat maps have proved that the key contribution of the attention module through visualization of the feature areas of images.

Citation Download Citation

Qinhong Zou, Yubin Sun, Jiongming Qin, Zeqiong Huang, and Bin Chen "Toward more efficient iris recognition using a lightweight CNN framework with attention mechanism", Proc. SPIE 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 122900F (3 October 2022); https://doi.org/10.1117/12.2640778

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