臺灣博碩士論文加值系統

這篇論文中，我們提出使用entropy-box-counting (EBC) 的新方法來分析碎形維度且應用於虹膜辨識系統。
本論文以碎形維度來表示虹膜紋理區域資訊。在影像前置處理中，將環狀的虹膜區域正規化轉換成矩形的區塊並且將此區塊再分成48個小區塊，然後計算這些區塊的碎形維度值並將其串連在一起作為一個虹膜特徵向量。我們以空間距離來測量兩個虹膜的相似程度，以最小距離分類器 (MDC) 來確定兩虹膜是否屬於相同類別。最後我們以CASIA資料庫所儲存之虹膜對系統進行測試，得到虹膜辨識率為97.69%和等錯誤率為11.57%，證明我們系統具有高度的有效性。

In this thesis, we propose a new method of fractal analysis using entropy-box-counting (EBC) for automatic iris recognition. First, the annular iris image is normalized into a rectangular iris image and divided into forty-eight blocks. We calculate the fractal dimension values for these image blocks and then concatenate all these features together as the iris feature vector. The similarity of two irises can be measured by the spatial distance. We use the Minimum Distance Classifier (MDC) to determine whether two irises belong to the same class. Experimental results show that the recognition rate is 97.69% and the equal error rate is 11.57% for the images in CASIA database.

1 Introduction 1
1.1 Motivation 1
1.2 Organization of the Thesis 2
2 Related Work 3
2.1 Iris Recognition Systems 3
2.2 Key Issues about Iris Recognition 4
2.2.1 Iris Image Preprocessing 4
2.2.2 Multi-Channel 2-D Gabor Filters 6
2.2.3 Key Points Extraction 7
2.2.4 Similarity Computation 8
3 Image Preprocessing 10
3.1 Iris Image Localization 10
3.1.1 Coarse Determination for Pupil’s Region 10
3.1.2 Noise Removal 12
3.1.3 Iris Localization 15
3.2 Iris Normalization 16
4 Feature Extraction and Matching 18
4.1 Fractal Analysis Based on Entropy-Box-Counting 19
4.2 Feature Matching 21
5 Experimental Results 22
5.1 Performance Evaluation 22
5.2 Test Results with CASIA Database 24
6 Conclusions and Future Work 28
References 29

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