Adaptive Contextual Learning Method Based on Iris Texture Perception

The microstructures in the iris exhibit a high degree of individual distinctiveness,making iris recognition an ideal choice for identity verification.In addition to the characteristics of the microstructures themselves,the context among them also serves as an effective cue for identity verification.To address the correlations between iris microstructures,an adaptive contextual learning method based on iris texture perception is proposed.This method improves upon the dual-ranch structure of contextual measures model by incorporating channel attention and efficient multi-scale attention mechanisms.These mechanisms dynamically adjust the feature maps adaptively,capturing features from different levels of detail distribution and enhancing sensitivity to iris microstructures.To thoroughly explore the correlation between global and local features,attention mechanisms are employed to adaptively fuse the features extracted by the dual-branch network.This weighting approach flexibly assigns diffe-rent weights based on the importance or relevance of the input,aiming to learn the optimal feature associations.The experimental results demonstrate that the adaptive contextual learning method performs excellently in iris recognition tasks,surpassing existing baseline methods across multiple evaluation metrics,with higher recognition accuracy and stronger generalization ability.