Robust iris recognition under unconstrained settings

Tese de mestrado integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Segmentation-level fusion for iris recognition

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms

Robust pre-processing techniques for non-ideal iris images

The human iris has been demonstrated to be a very accurate, non-invasive and easy-to-use biometric for personal identification. Most of the current state-of-the-art iris recognition systems require the iris acquisition to be ideal. A lot of constraints are hence put on the user and the acquisition process.;Our aim in this research is to relax these conditions and to develop a pre-processing algorithm, which can be used in conjunction with any matching algorithm to handle the so-called non-ideal iris images. In this thesis we present a few robust techniques to process the non-ideal iris images so as to give a segmented iris image to the matching algorithm. The motivation behind this work is to reduce the false reject rates of the current recognition systems and to reduce the intra-class variability. A new technique for estimating and compensating the angle in non-frontal iris images is presented. We have also developed a novel segmentation algorithm, which uses an ellipse-fitting approach for localizing the pupil. A fast and simple limbus boundary segmentation algorithm is also presented

Feature Matching in Iris Recognition System using MATLAB

Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software

Color space analysis for iris recognition

This thesis investigates issues related to the processing of multispectral and color infrared images of the iris. When utilizing the color bands of the electromagnetic spectrum, the eye color and the components of texture (luminosity and chromaticity) must be considered. This work examines the effect of eye color on texture-based iris recognition in both the near-IR and visible bands. A novel score level fusion algorithm for multispectral iris recognition is presented in this regard. The fusion algorithm - based on evidence that matching performance of a texture-based encoding scheme is impacted by the quality of texture within the original image - ranks the spectral bands of the image based on texture quality and designs a fusion rule based on these rankings. Color space analysis, to determine an optimal representation scheme, is also examined in this thesis. Color images are transformed from the sRGB color space to the CIE Lab, YCbCr, CMYK and HSV color spaces prior to encoding and matching. Also, enhancement methods to increase the contrast of the texture within the iris, without altering the chromaticity of the image, are discussed. Finally, cross-band matching is performed to illustrate the correlation between eye color and specific bands of the color image

An approach towards iris localization for non cooperative images: A study

Iris localization is the most important part of iris recognition which involves the detection of iris boundaries in an image. A very important need of this effective security system is to overcome the rigid constraints necessitated by the practical implementation of such a system. There are a few existing techniques for iris segmentation in which iris detection using Circular Hough Transform is the most reliable and popular and it has been implemented in this project. But there is a shortcoming in this technique. It does not perform well and does not gives high accuracy with images containing noise or occlusions caused by eyelids. Such kind of images constitute non cooperative data for iris recognition. To provide acceptable measures of accuracy, it is critical for an iris recognition system to overcome various noise effects introduced in images captured under different environment such as occlusions due to eyelids. This report discusses an approach towards less constraint iris recognition using occluded images. The Circular Hough Transform is implemented for few images and a novel approach towards iris localization and eyelids detection is studied.