Petru Radu

Are Two Eyes Better than One? An Experimental Investigation on Dual Iris Recognition

Iris recognition using both eyes of an individual has not been extensively researched in the available literature. However, for iris recognition at a distance, capturing a good quality image of the same eye every time is a challenging task and a dual iris approach is potentially beneficial. In the present work, we investigate the advantages of using dual iris approach for iris recognition, comparing the performances of using both eyes with those obtained using only one eye.

A novel iris clustering approach using LAB color features

Interesting results of color clustering for the iris images in the UBIRISv1 database are presented. The iris colors are characterized by feature vectors with 80 components corresponding to histogram bins computed in the CIELAB color space. The feature extraction is applied to the first session eye images after undergoing an iris segmentation process. An agglomerative hierarchical algorithm is used to organize 1.205 segmented iris images in 8 clusters based on their color content.

A review of information fusion techniques employed in iris recognition systems

Iris recognition has shown to be one of the most reliable biometric authentication methods. The majority of iris recognition systems which have been developed require a constrained environment to enrol and recognise the user. If the user is not cooperative or the capture environment changes then the accuracy of the iris recognition system may decrease significantly. To minimise the effect of such limitations, possible solutions include the use of multiple channels of information such as using both eyes or extracting more iris feature types and subsequently employing an efficient fusion method. In this paper, we present a review of iris recognition systems using information from multiple sources that are fused in different ways or at different levels. A categorisation of the iris recognition systems incorporating multiple classifier systems is also presented. As a new desirable dimension of a biometric system, besides those proposed in the literature, the mobility of such a system is introduced in this work. The review charts the path towards greater flexibility and robustness of iris recognition systems through the use of information fusion techniques and points towards further developments in the future leading to mobile and ubiquitous deployment of such systems.

A Visible Light Iris Recognition System using Colour Information

The iris has been shown to be a highly reliable biometric modality with almost perfect authentication accuracy. However, a classical iris recognition system operates under near infrared illumination, which is a major constraint for a range of applications. In this paper, we propose an iris recognition system which is able to cope with noisy colour iris images by employing image processing techniques together with a Multiple Classifier System to fuse the information from various colour channels. There are two main contributions in the present work: first, we adapt standard iris features, proposed in the literature for near infrared images, to match the characteristics of colour iris images; second, we introduce a robust fusion mechanism to combine the features from various colour channels. With a feature size of only 360 real numbers, the efficiency of the proposed biometric system is demonstrated on the UBIRISv1 dataset for both identification and verification scenarios.

On combining information from both eyes to cope with motion blur in Iris Recognition

Iris Recognition has emerged as one of the best biometric authentication techniques in recent years. However, a significant drawback of this biometric modality is the constrained environment in which the user is enrolled and recognized. It typically requires the user to be very cooperative for good quality images to be captured. If this limitation could be effectively addressed, it would be possible to employ iris recognition in environments where images incorporating increased noise and distortions were present whilst maintaining high recognition accuracy. In the present paper, we explore how the effect of image distortions caused by motion blur may be significantly reduced by using iris information from both eyes of the user.

Image Enhancement vs Feature Fusion in Colour Iris Recognition

In iris recognition, most of the research was conducted on operation under near infrared illumination. For an iris recognition system to be deployed on common hardware devices, such as laptops or mobile phones, its ability of working with visible spectrum iris images is necessary. Two of the main possible approaches to cope with noisy images in a colour iris recognition system are either to apply image enhancement techniques or to extract multiple types of features and subsequently to employ an efficient fusion mechanism. The contribution of the present paper consists of comparing which of the two above mentioned approaches is best in both identification and verification scenarios of a colour iris recognition system. The efficiency of the two approaches is demonstrated on UBIRISv1 dataset.

A Multi-algorithmic Colour Iris Recognition System

The reported accuracies of iris recognition systems are generally higher on near infrared images than on colour RGB images. To increase a colour iris recognition system’s performance, a possible solution is a multialgorithmic approach with an appropriate fusion mechanism. In the present work, this approach is investigated by fusing three algorithms at the score level to enhance the performance of a colour iris recognition system. The contribution of this paper consists of proposing 2 novel feature extraction methods for colour iris images, one based on a 3-bit encoder of the 8 neighborhood and the other one based on gray level co-occurrence matrix. The third algorithm employed uses the classical Gabor filters and phase encoding for feature extraction. A weighted average is used as a matching score fusion. The efficiency of the proposed iris recognition system is demonstrated on UBIRISv1 dataset.

Optimizing 2D Gabor Filters for Iris Recognition

The randomness and richness present in the iris texture make the 2D Gabor filter bank analysis a suitable technique to be used for iris recognition systems. To accurately characterize complex texture structures using 2D Gabor filters it is necessary to use multiple sets of parameters of this type of filters. This paper proposes a technique of optimizing multiple sets of 2D Gabor filter parameters to gradually enhance the accuracy of an iris recognition system. The proposed methodology is suitable to be applied on both near infrared and visible spectrum iris images. To illustrate the efficiency of the filter bank design technique, UBIRISv1 database was used for benchmarking.

Nomad Biometric Authentication: Towards Mobile and Ubiquitous Person Identification

Our work and living environments are increasingly populated by devices which integrate computational ability and intelligence. The Nomad Biometric Authentication (NOBA) project focuses on the development and implementation of biometric technologies for strong authentication to enable provision of nomadic computing users with services and document dematerialization in secure information and communication systems. NOBA adopted visual capture of biometric information, aiming to achieve, as much as possible, data acquisition which is unobtrusive and transparent to the user. Therefore, the modalities chosen are iris analysis (in low resolution, as it is either based on mobile device capture, or capture at a distance), analysis of face and its characteristic parts, and analysis of gait. Thus, the objective of the NOBA project is to demonstrate the feasibility of a nomad strong authentication service on a distributed environment of ubiquitous and mobile devices, based on acquiring and merging biometric data.