Robust Recognition Of Heterogeneous Iris Images

The Visual Internet of Things and Mobile-Internet provide ubiquitous sensors and processors for acquisition and recognition of iris images. However, in this scenario, it is inevitable to capture a large number of heterogeneous iris images with quality and condition variations. In this thesis, the theory and method for robust recognition of heterogeneous iris images are systematically studied. First, blur and low resolution which are of great importance in heterogeneous iris recognition are discussed respectively, and then a general framework is proposed. The main contributions are as follows:· Blurred iris images without texture details degrade recognition performance. A novel deblurring algorithm based on point spread function (PSF) refine-ment is proposed to alleviate the visual difference between clear and blurred iris images. First, the input images are classified to be defocus or motion blurred, and PSF is initialized based on parametric models. Second, PSF is refined in pixel level by iteratively optimization of PSF, support regions and the latent image. Finally, the deblurred images are applied for recognition, which improves recognition accuracy.· As recognition samples, iris images should be deblurred with emphasis on recognition items rather than visual effects. Hence, a hierarchical model is proposed to adaptively assign prior learning methods to regions with d-ifferent usages, which enables both visual and machine perceptions in iris images. The latent variable based formulation incorporates the hierarchical model and other task-specific terms, which guarantees its flexibility.· The recognition performance of motion blurred images after restoration is shown can be further improved. Experiments are conducted to reveal the relation between distortion and performance. A mask-based matching s-trategy is proposed. In the first type of mask generation, this observation is used to generate the mask. In the second type, based on training samples, the reliability of each code bit is estimated and used for mask generation.· Apart from image blur, low resolution (LR) is another important problem in heterogeneous iris recognition. A metric learning based algorithm is pro-posed as the solution in metric space. In the method, an ideal mapping is defined to collapse heterogeneous (High-resolution vs. LR) and homoge-neous (HR vs. HR) samples, and keep identically labeled samples close while separating samples in different classes. The target Mahalanobis distance is learnt to capture the information in ideal mapping as much as possible. Based on this learnt metric, it is easier to sperate intra-and inter-class samples, which enhances recognition performance.· Aiming at a general solution, we propose a code-level information mapping algorithm. It can map the probe-state iris codes into the corresponding reg-istered states and then uses the mapped codes for recognition. Code-level operations are sandwiched between feature and score levels. The modified Markov model is employed to model the nonlinear relationship between het-erogeneous codes. Meanwhile, the maximum compatibility value is used to measure the bit reliability and optimized to form a matching mask. This algorithm is also extend into the situation of multi-source heterogeneity.In this thesis, we focus on heterogeneous iris recognition and follow the typical recognition procedure. According to different sources of heterogeneity, solutions are proposed to make the recognition systems more tolerant to heterogeneous samples, which contributes to the research area of heterogeneous iris recognition.