Cascade attention feature residual fusion network for iris localization and segmentation in non-cooperative environments

Iris localization and segmentation constitute mission-critical preprocessing stages in iris recognition systems, where their precision directly governs overall recognition accuracy. However, iris images captured under non-cooperative conditions are prone to boundary distortions caused by eyelash or eyelid occlusions and defocus blurring, while texture features suffer from weakened saliency due to uneven illumination or specular reflections, leading to reduced algorithm robustness. To address these challenges, this paper proposes a cascade attention feature residual fusion network (CA-RFNet) for multitask iris localization and segmentation in unconstrained scenarios. CA-RFNet adopts an encoder-decoder structure with skip connections. In the encoder stage, deep convolutional residual blocks hierarchically extract iris texture features. A cascade attention fusion module embedded in skip connections dynamically weights and adaptively integrates multi-receptive-field features while enabling cross-scale information complementarity. The decoder incorporates a boundary perception module with cross-layer feature interaction mechanisms to enhance fine-grained structural perception and cross-hierarchy semantic representation, thereby improving edge prediction accuracy. CA-RFNet modules work collaboratively to overcome adverse effects of unconstrained subject behaviors and complex environmental interference on algorithm robustness in non-cooperative scenarios. Extensive experiments on five non-cooperative iris datasets (CASIA-Iris-Distance, CASIA-Iris-Complex-Occlusion, CASIA-Iris-Complex-Off-angle, CASIA-Iris-M1, and CASIA-Iris-Africa) demonstrate that CA-RFNet achieves superior segmentation and localization performance on challenging samples with complex noise factors including occlusion, off-angle, illumination variation, specular reflection, dark iris, and dark skin.