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

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.