基于改进YoloV4网络的虹膜定位算法

针对传统虹膜定位算法很难完成准确定位导致识别效果不稳定的问题, 提出一种基于改进YoloV4网络的虹膜定位算法. 首先利用YoloV4结合MobileNetV3对虹膜内外圆进行粗定位, 再利用瞳孔、 虹膜和巩膜的灰度差值分别对虹膜内外圆进行精定位. 同时, 使用K-means++聚类算法生成先验框； 使用快速soft-DIoU-NMS算法去除预测过程冗余框, 提高算法检测率； 使用Focal Loss作为类别损失函数. 对比实验结果表明, 该算法运行速度更快, 定位准确率更高, 识别算法的效果更明显.

Iris Location Algorithm Based on Improved YoloV4 Network

Aiming at  the problem that the traditional iris location algorithm was difficult to achieve accurate location, resulting in unstable recognition effect, we proposed an iris location algorithm based on improved YoloV4 network. Firstly, YoloV4 combined with MobileNetV3 was used for rough positioning of the inner and outer circles of the iris, and then the gray difference between the pupil, iris and sclera was used for fine positioning of the inner and outer circles of the iris. At the same time, K-means++ clustering algorithm was used to generate a priori box. The fast soft-DIoU-NMS algorithm was used to remove the redundant boxes in the prediction process and improve the detection rate of the algorithm. The Focal Loss was used as a category loss function. The comparative experimental results show that the algorithm runs faster, the location accuracy is higher, and the effect of recognition algorithm is more obvious.