Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8627
標題: 應用賈伯濾波器於虹膜辨識系統
Iris Recognition Using Gabor Filters
作者: 蔡仲智
Tsai, Chung-Chih
關鍵字: Iris recognition;虹膜辨識;Gabor filter;Feature selection;賈伯濾波器;特徵選取
出版社: 電機工程學系所
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摘要: 
基於虹膜的高度獨特性與穩定性,使虹膜辨識系統成為生物特徵領域中受矚目的研究題目。虹膜辨識的主要挑戰在於如何萃取出較小的特徵向量,同時又能充分表現出虹膜影像的特性,進而達成高效能之辨識系統。在本論文中,我們提出三種虹膜特徵萃取方法,並且利用設計的特徵萃取方法建構出不同的虹膜辨識系統。在影像前置處理模組中,我們將虹膜下半部影像分割、展開並且正規化成一個矩形區塊影像。接著,分別利用提出的三種特徵萃取方法,在正規化的虹膜區塊中抽取出虹膜特徵向量。
在本論文提出的第一組虹膜系統中,我們結合了一組賈伯濾波器與碎形維估測法作為虹膜特徵萃取模組。首先,運用多通道的賈伯濾波器對正規化的虹膜區塊分解產生輸出影像。接著,利用差值方盒計數法在濾波器輸出影像中估測小區域的碎形維度。在第二種系統中,同樣利用賈伯濾波器強化虹膜影像。然後藉由分析每張濾波器輸出影像水平方向的相對變化性,產生一組精簡的虹膜特徵碼。此外,以上兩組辨識系統皆採用了一個簡單的特徵選取演算法去除多餘的虹膜資訊,同時提升系統辨識率。在第三組辨識系統中,我們提出了一個賈伯濾波器最佳化的方法。我們利用粒子群最佳化演算法產生最佳化賈伯濾波器的參數,並且發展一套連續法來決定對於特定問題所需要的濾波器數量。第三種虹膜辨識系統利用最佳化的賈伯濾波器來萃取虹膜特徵,並且使用相對變化分析法產生一組更為精簡且有效的虹膜碼。實驗結果顯示,本論文所提出的虹膜辨識系統能產生較小的特徵向量同時達到極高的辨識率。

Iris recognition is an active and popular research topic in biometrics. A major challenge of the iris recognition is to extract the sufficient information from distinctive iris texture and to generate a compact iris code. In this dissertation, three iris feature extraction methods are proposed to build the different iris recognition systems. In the preprocessing stage, the lower part of the iris is unwrapped and normalized to a rectangular block. After that, the proposed feature extraction algorithms are applied to generate the iris feature vector.
In our first system, we adopt a bank of Gabor filters combined with the fractal dimension estimation to extract the iris features. The normalized iris block is first decomposed by the multi-channel Gabor filters. Then the differential box-counting method is used to estimate the fractal dimension of the filtered images. The Gabor decomposition is also adopted in the second proposed system in which the iris code is generated by analyzing and encoding the horizontal variation of each filtered image. Moreover, a sequential floating search scheme is used to discard the redundant features and improve the system performance in both first and second systems. In the third proposed system, the parameters of the Gabor filters are optimized by the particle swarm optimization. In addition, a sequential scheme is developed to determine the number of filters for a particular application. During the iris feature extraction, the normalized iris image is decomposed by the optimized Gabor filters and then a compact iris code is generated by using the relative variation analysis. Experimental results show that each proposed approach can produce comparable performance to existing methods.
URI: http://hdl.handle.net/11455/8627
其他識別: U0005-2406200915402600
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