Robust iris localization algorithm based on improved YOLOv4 and self-organized particle swarm optimization

Abstract

Content Aiming at the problem that iris images are easily affected by eyelid and eyelash noise, which leads to low positioning accuracy and poor stability, an iris positioning network based on YOLOv4 model is proposed. Firstly, the backbone feature extraction network combining ECA attention mechanism and Densenet121 is introduced to enhance the feature extraction capability of the model. Secondly, the SPP module is improved and replaced with a deep detachable void space convolution pool pyramid to promote the information interaction between low-level location information and high-level semantic features. Finally, a self-organizing particle swarm optimization algorithm is designed to obtain the prior frame size that can characterize the overall features of iris data set. The proposed method was tested on the CIASIA-IrisV4 iris dataset, and the average accuracy of the improved model was 99.70%, and the positioning accuracy of the improved model was 95.89% in the case of light reflection generated by glasses. Compared with other iris positioning models, the accuracy and robustness were higher, which had obvious advantages.