Iris Recognition Algorithm Based On Multiresolution Analysis

With the development of information technology, the importance of personal authentication is becoming increasingly apparent. Higher and higher requirements are imposed on it from national security to everyone's daily life. For our country with so large a population, identity authentication has broad application prospects and strategic importance. Biometric identification, as an authentication technology with flourishing development at the end of the last century, makes use of a person's physiological and behavior characteristics to determine personal identity. It plays an increasingly important role in our social life and will gradually take the place of the traditional identification methods. Iris recognition is a biometric recognition technology emerging in recent years. It has attracted the attentions of academic and business community for its advantages such as high accuracy, non-contact collection, easy to use etc. It is widely considered as the most promising biometric recognition technology in the future.Multi-resolution analysis is a very important tool in modern digital image processing. It can represent an image in a multi-scale manner and can be used to find the image characteristics and extract image features unavailable in the pixel domain. With Iris recognition technology as the topic and based on the deeply study on the multi-resolution theory and the main features of iris recognition, this thesis researches on the efficient feature extraction algorithms of Iris images under multi-resolution framework and corresponding preprocessing and feature matching algorithms. Firstly, the principle of biometric recognition and the commonly used biometric recognition methods are introduced briefly, with emphasis on the main features, history, the key components and the current research status of iris recognition technology. Then, after investigating the existing preprocessing methods in depth, a normalization method for the Iris image is proposed which is more suitable for extracting the texture features from the 2D Iris image. Subsequently, based on the Multi-scale Geometric Analysis (MGA) theory, the classic contourlet transform is adopted to extract the multi-scale and multi-directional information as the feature vector, and support vector machine is used to classify these features for recognition. Next, for the deficiency of the Laplacian pyramid in contourlet transform, after comparing the commonly used multi-resolution analysis methods, a superior circular symmetric filter is applied to decompose the Iris image in order to get more stable and accurate Iris features and thus better the recognition performance. In addition, an adaptive non-stationary signal analysis method-empirical mode decomposition is used for Iris recognition, the frequency bands most suitable for recognition are identified through experiments and ideal recognition performance is achieved.The main innovations of this thesis include:1. A normalization scheme of the iris image is proposed with non-polar expansionIn traditional methods, the Iris image is normalized under polar coordinate, the annular-like Iris image is mapped into a rectangular region of fixed size along the angular direction, in this way, the scale and translation invariance can be achieved. This method is simple and effective, so it is widely used in all previous Iris recognition systems. However, because of the difference between the radii of inner and outer circles, a large number of interpolation and decimation operations must be taken during the expansion process, the texture structure and directional information of the Iris image are inevitably altered. And what's more, in order to avoid the regions with eyelids and eyelashes occlusion, the available region is just a narrow rectangular image, this may take little effect on the feature extraction methods of using local information or transforming the Iris image into a 1D signal. But surely, it will make a negative impact on the algorithms with global information or directional information as the features. To solve this problem, a normalization method is proposed in which there is no need to expand the polar coordinate into a rectangular. This method can preserve the original geometric structure and directional information of the Iris texture, and a reverse mapping strategy is used to fill in the region in the inner circle avoiding introducing new boundaries. The obtained normalized image is more balanced in size, and the effective region for Iris recognition can be selected flexibly according to the acquisition quality, which provides more choices for the feature extraction methods using multi-scale geometric analysis and other methods.2. An Iris recognition system based on contourlet transform and SVM is proposedMulti-scale Geometric Analysis methods have superior performance in sparsely representing the 2D image. In this thesis, contourlet transform is used to extract the global texture information of an Iris image, the normalized energies of the multi-scale and multi-directional subbands act as the feature vector, and the SVM is adopted for recognition. Experiments have been designed to try every possible combination of the decomposition level and number of directions used in an exhaustive manner, and the recognition rates under all schemes are listed, which can provide an instruction for further research on the Iris recognition systems based on MGA.3. An Iris feature extraction method based on an improved circular symmetric multi-resolution decomposition scheme is proposedThe Multi-resolution analysis theory has become the indispensable tool in image processing community. The analysis process under different resolutions has advantageous for pattern recognition. Based on the comparisons of the classic pyramid decomposition algorithms, an improved circular symmetric filter bank is used for Iris image decomposition. It avoids the frequency aliasing effect and owns translation and rotation invariance, which makes the Iris information in different scales more accurate and stable. Then the directional filter bank is adopted to get the directional information for recognition. Experimental results show that the proposed scheme can achieve better recognition rates.4. An Iris recognition approach based on empirical mode decomposition is proposedThe empirical mode decomposition(EMD) can adaptively decompose a non-stationary, nonlinear signal into a series of stationary and linear intrinsic mode functions(IMF). These IMFs are independent of each other and reflect the local characteristics of a signal. EMD can be regarded as a multi-resolution decomposition method with each IMF corresponding to a different frequency band. In the proposed method, the Iris image is decomposed by EMD into a few IMFs from high to low frequencies, then the IMFs or their combinations most suitable for recognition are determined with mutual information criterion as a primitive evaluation and large amount of experimental analysis. The selected IMF or IMFs are coded effectively and Hamming distance is adopted as the matching algorithm for recognition. Experiment results show that this method can not only simplify the preprocessing procedure, but can achieve surprisingly high recognition performance.The work in this thesis can be regarded as some useful exploration and attempt in order to enrich the existing Iris recognition algorithms. Experimental results show that the proposed iris recognition algorithms can basically meet the requirements of practical applications. Based on this work, further deep researches can be conducted to improve the algorithms' performance, and an iris recognition system with independent intellectual property of China is expected to be developed.further research can be carried out based on it.