Biometric template protection techniques have been proposed to address security and privacy issues inherent to biometric-based authentication systems. However, it has been shown that the robustness of most of such techniques against reversibility and linkability attacks are overestimated. Thus, a thorough security analysis of recently proposed template protection schemes has to be carried out. Negative iris recognition is an interesting iris template protection scheme based on the concept of negative databases. In this paper, we present a comprehensive security analysis of this scheme in order to validate its practical usefulness. Although the authors of negative iris recognition claim that their scheme possesses both irreversibility and unlinkability, we demonstrate that more than 75% of the original iris-code bits can be recovered using a single protected template. Moreover, we show that the negative iris recognition scheme is vulnerable to attacks via record multiplicity where an adversary can combine several transformed templates to recover more proportion of the original iris-code. Finally, we demonstrate that the scheme does not possess unlinkability. The experimental results, on the CASIA-IrisV3 Interval public database, support our theory and confirm that the negative iris recognition scheme is susceptible to reversibility, linkability, and record multiplicity attacks.

In this Letter, a new iris recognition approach based on local Gabor orientation feature is proposed. On one hand, the iris feature extraction method using the traditional Gabor filters can cause time-consuming and high-feature dimension. On the other hand, we can find that the changes of original iris texture in angle and radial directions are more obvious than the other directions by observing the iris images. These changes in the preprocessed iris images are mainly reflected in vertical and horizontal directions. Therefore, the local directional Gabor filters are constructed to extract the horizontal and vertical texture characteristics of iris. First, the iris images are preprocessed by iris and eyelash location, iris segmentation, normalization and zooming. After analyzing the variety of iris texture and 2D-Gabor filters, we construct the local directional Gabor filters to extract the local Gabor features of iris. Then, the Gabor & Fisher features are obtained by Linear Discriminant Analysis (LDA). Finally, the nearest neighbor method is used to recognize the iris. Experimental results show that the proposed method has better iris recognition performance with less feature dimension and calculation time.

We previously proposed a rotation-spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape, and its shape irrespective of its orientation. The R-SAN net is suitable for orientation recognition of a concentric circular pattern such as an iris image. Previously, variations of ambient lighting conditions affected iris detection. In this study, we introduce brightness normalization for accuracy improvement of iris detection in various lighting conditions. Brightness normalization provides high accuracy iris extraction in severe lighting conditions. A recognition experiment investigated the characteristics of rotation and shape recognition for both learned and un-learned iris images in various plane rotations. The R-SAN net recognized the rotation angle for the learned iris images in arbitrary orientation, but not for un-learned iris images. Thus, the variation of the rotation angle was corrected only for learned irises, but not un-learned irises. Although the R-SAN net rightly recognized the learned irises, it could not completely reject the un-learned irises as unregistered irises. Using the specific orientation recognition characteristics of the R-SAN net, a minimum distance was introduced as a new shape recognition criterion for the R-SAN net. In consequence, the R-SAN net combined with the minimum distance rightly recognized both learned (registered) and un-learned irises; the unregistered irises were correctly rejected.

In a conventional method based on quadrature 2D Gabor wavelets to extract iris features, the iris recognition is performed by a 256-byte iris code, which is computed by applying the Gabor wavelets to a given area of the iris. However, there is a code redundancy because the iris code is generated by basis functions without considering the characteristics of the iris texture. Therefore, the size of the iris code is increased unnecessarily. In this paper we propose a new feature extraction algorithm based on independent component analysis (ICA) for a compact iris code. We implemented the ICA to generate optimal basis functions which could represent iris signals efficiently. In practice the coefficients of the ICA expansions are used as feature vectors. Then iris feature vectors are encoded into the iris code for storing and comparing individual's iris patterns. Additionally, we introduce a method to refine the ICA basis functions for improving the recognition performance. Experimental results show that our proposed method has a similar equal error rate as a conventional method based on the Gabor wavelets, and the iris code size of our proposed methods is five times smaller than that of the Gabor wavelets.

Iris recognition is to identify a user based on the iris texture information which exists between the white sclera and the black pupil. Iris recognition system has been in the limelight for high-security biometric applications due to the advantages of non-contact characteristics and the highest recognition performance among biometric systems. Conventional iris recognition systems consist of the iris camera and the processing unit, like a PC or an embedded control box. The iris camera captures the user's iris images and transfers them to the processing unit. In the processing unit, the captured images are processed and recognition is performed. For fast recognition, it is very important to capture the user's focused eye image at fast speed. If not, the total recognition time is increased and it makes the user feel much inconvenience. In previous researches and systems, they use the focusing method which has been used for general landscape scenes without considering the characteristics of iris image. So, they take much focusing time sometimes, especially in the case of the user with glasses. To overcome such a problem, we propose a new iris image acquisition method to capture the user's focused eye image at very fast speed. It can be applicable to the users both with and without glasses.

Iris recognition is used to identify a user based on the iris texture information which exists between the white sclera and the black pupil. For fast iris recognition, it is very important to capture user's focused eye image at fast speed. If not, the total recognition time is increased and it makes the user feel much inconvenience. In previous researches and systems, they use the focusing method which has been used for general landscape scene without considering the characteristics of iris image. So, they take much focusing time sometimes, especially in case of the user with glasses. To overcome such problems, we propose a new iris image acquisition method to capture user's focused eye image at very fast speed based on the corneal specular reflection. Experimental results show that the focusing time for both the users with glasses and without glasses is average 480 ms and we can conclude our method can be used for the real-time iris recognition camera.