可處理線性與非線性虹膜紋理形變之虹膜辨識方法

本篇論文中，我們探討了非正向視角和虹膜紋理形變對於虹膜辨識系統的影響，也提出了有效的辦法來解決這兩項問題。首先，我們提出了一套非正向視角虹膜辨識系統，其中包含了新的虹膜取像模組、虹膜定位模組、虹膜特徵抽取模組和分類模組。我們開發了一台雙CCD 攝影機用來擷取四頻譜(紅、綠、藍和近紅外線) 虹膜影像，四頻譜虹膜影像提供了可用的影像資訊用以簡化虹膜定位的工作。智慧型RANSAC 虹膜定位方法用於四頻譜虹膜影像中準確的偵測出虹膜邊界。為了比對不同視角所拍攝到的虹膜影像，我們提出圓形矯正的方法以減少不同視角下虹膜的變形，其校正參數可經由偵測到的橢圓瞳孔邊界估測得到。此外，我們提出一套新穎的虹膜描述器用來描述多重尺度的step/ridge 邊的虹膜紋理。這些邊可以經由 Derivative of Gaussian 和Laplacian of Gaussian 所組成的濾波器來描述。實驗結果顯示，透過我們的方法在辨識 ±30◦ 內之非正向視角的虹膜影像的等差錯誤率只有0.04%。除此之外，我們也提出了一套非線性虹膜紋理正規化的方法，該方法可用於處理因為瞳孔縮小/放大所造成的虹膜紋理形變。為了證明我們方法的可行性，同時也設計了另一套虹膜影像擷取系統，此系統可經由微控制器提供電流源藉以控制藍色LED 陣列所產生的可見光亮度，用意在於擷取不同亮度下虹膜紋理形變的影像。實驗結果顯示，我們所提出的非線性虹膜正規化方法的等差錯誤率是0.95% 勝過傳統線性虹膜影像正規化方法的2.76%。

關鍵字

虹膜辨識；虹膜取像系統；非正向視角虹膜辨識；非理想虹膜辨識；虹膜定位；虹膜正規化；虹膜特徵擷取；虹膜紋理形變；線性虹膜正規化；非線性虹膜正規化

並列摘要

In this dissertation, we study the influences of both the non-orthogonal imaging condition and the nonlinear deformation of iris patterns on the accuracy of iris recognition, and we also propose effective methods to deal with these two topics. First, we propose a non-orthogonal view iris recognition system which comprises a new iris imaging module, an iris segmentation module, an iris feature extraction module and a classification module. A dual-CCD camera is developed to capture four-spectral (red, green, blue and near-infrared) iris images which contain useful information for simplifying the iris segmentation task. An intelligent RANSAC iris segmentation method is proposed to robustly detect iris boundaries in a four-spectral iris image. In order to match iris images acquired at different off-axis angles, we propose a circle rectification method to reduce the off-axis iris distortion. The rectification parameters are estimated using the detected elliptical pupillary boundary. Furthermore, we propose a novel iris descriptor which characterizes an iris pattern with multi-scale step/ridge edge-type maps. The edge-type maps are extracted with the derivative of Gaussian and the Laplacian of Gaussian filters. The iris pattern classification is accomplished by edge-type matching which can be understood intuitively with the concept of classifier ensembles. Experimental results show that the equal error rate of our approach is only 0.04% when recognizing iris images acquired at different off-axis angles within ±30◦. Additionally, a nonlinear iris normalization method is proposed. This method can handle iris deformation due to myosis/mydriasis. In order to prove the feasibility of our method, another iris imaging system is constructed. This system includes a computer controllable current source for driving a blue LED array, which is used to capture iris deformation images at different light intensity levels. Experimental result shows that our proposed method outperforms the traditional linear normalization method. The equal error rates of our and the traditional linear normalization method are 0.95% and 2.76%, respectively.

並列關鍵字

Iris Recognition ； Iris Imaging System ； Non-orthogonal View Iris Recognition ； Nonideal Iris Recognition ； Iris Segmentation ； Iris Normalization ； Iris Feature Extraction ； Deformation of Iris Patterns ； Linear Iris Normalization ； Nonlinear Iris Normalization