虹膜區域切割是整個虹膜識別流程中關鍵的步驟，大多數目前先進的虹膜區域切割演算法皆是建立於圖像的邊緣信息，然而，一般基於邊緣信息的檢測器會在出現鏡面反射或其他障礙物的圖像上產生過多影響定位虹膜內外邊界的雜訊點。本文提出了一種結合邊緣信息以及基於學習的混合型虹膜區域切割演算法，使用了僅有六層且設計良好的Faster R-CNN來定位且識別圖像上的眼睛，根據Faster R-CNN找到的區域邊界框，利用高斯混合模型定位瞳孔區域，最後透過五個關鍵的虹膜內邊界點擬合出虹膜內邊界圓，再以改良後的MIGREP演算法和邊界點選擇演算法找尋虹膜外邊界圓的邊界點，由這些找到的虹膜外邊界點擬合出虹膜外邊界圓。實驗結果顯示了本文所提出的演算法在具挑戰性的CASIA-Iris-Thousand資料庫上達到95.49%精確度。;Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points created by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. In this paper, we present a combination method of learning-based and edge-based algorithms for iris segmentation. A well-designed Faster R-CNN with only six layers is built to locate and classify the eye. With the bounding box found by Faster R-CNN, the pupillary region is located using a Gaussian mixture model. Then, the circular boundary of the pupillary region is fit according to five key boundary points. The enhanced version of MIGREP and a boundary point selection algorithm are used to find the boundary points of limbus, and the circular boundary of limbus is constructed using these bounding points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.
