Soumyadip Rakshit

DCT-Based Iris Recognition

This paper presents a novel iris coding method based on differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from normalized iris images. The feature extraction capabilities of the DCT are optimized on the two largest publicly available iris image data sets, 2,156 images of 308 eyes from the CASIA database and 2,955 images of 150 eyes from the Bath database. On this data, we achieve 100 percent correct recognition rate (CRR) and perfect receiver-operating characteristic (ROC) curves with no registered false accepts or rejects. Individual feature bit and patch position parameters are optimized for matching through a product-of-sum approach to Hamming distance calculation. For verification, a variable threshold is applied to the distance metric and the false acceptance rate (FAR) and false rejection rate (FRR) are recorded. A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical equal error rate (EER) is predicted to be as low as 2.59 times 10-1 available data sets

An Evaluation of Image Sampling and Compression for Human Iris Recognition

The resilience of identity verification systems to subsampling and compression of human iris images is investigated for three high-performance iris-matching algorithms. For evaluation, 2156 images from 308 irises from the extended Chinese Academy of Sciences Institute of Automation database were mapped into a rectangular format with 512 pixels circumferentially and 80 radially. For identity verification, the 48 rows that were closest to the pupil were taken and images were resized by subsampling their Fourier coefficients. Negligible degradation in verification is observed if at least 171 circumferential and 16 radial Fourier coefficients are preserved, which would correspond to sampling the polar image at 342 times 32 pixels. With JPEG2000 compression, improved matching performance is observed down to 0.3 b/pixel (bpp), attributed to noise reduction without a significant loss of texture. To ensure that the iris-matching algorithms studied are not degraded by image compression, it is recommended that normalized iris images should be exchanged at 512 times 80 pixel resolution, compressed by JPEG 2000 to 0.5 bpp. This achieves a smaller file size than the ANSI/INCITS 379-2004 iris image interchange format.

Eyelash Removal Method for Human Iris Recognition

A novel eyelash removal method for preprocessing of human iris images in a human iris recognition system is presented. The method filters each occluded pixel along an axis perpendicular to the eyelash direction, and accepts the filtered value if it changes by more than a certain threshold. This allows partially occluded regions of the iris to be included in iris coding which would previously have been excluded. The method is applied with three iris coding algorithms on an extended 308 class CASIA database and large improvements are shown in the matching performance of two methods, with a modest improvement in the third.

Effects of Sampling and Compression on Human IRIS Verification

The resilience of identity verification systems to subsampling and compression of human iris images is investigated for three high performance iris matching algorithms. For evaluation, 2156 images from 308 eyes are mapped into a rectangular format with 512 pixels circumferentially and 80 radially. For identity verification, the 48 rows nearest the pupil were taken and the images were subsampled by Fourier domain processing. Negligible degradation in verification is observed if at least 171 circumferential and 16 radial Fourier coefficients are preserved, corresponding to sampling at 342 by 32 pixels. With compression by JPEG 2000, improved performance is observed down to 0.3 bpp, attributed to noise reduction without significant loss of texture. To ensure that no algorithm is degraded, it is recommended that normalized iris images should be exchanged at 512 times 80 pixel resolution, compressed by JPEG 2000 to 0.5 bpp. This achieves a smaller file size than the proposed M1 biometric data interchange format

Medical Conditions: Effect on Iris Recognition

A first investigation into the effects of cataract surgery and pupil dilation on iris recognition performance is presented. Images from 3 patients were acquired before cataract surgery and two weeks afterwards. Successful iris segmentation was achieved in all cases despite additional specular reflections in the pupil due to the artificial lens. No visible change in iris structure was noticed and automatic matching of pre and postoperative images produced correct identification is all cases. To analyze the effect of dilation, images from 11 subjects were captured before administration of drops and fifteen minutes afterwards. Significant changes in pupil structure were observed with time and the deformation was found to be non-elastic in most cases. A novel shape-description method was used to model irregular pupil outlines, since conventional circle-based localization methods proved inadequate. Despite accurate localization, highly deformed irises failed to match correctly and the CRR obtained was only 86.67%. The results question the widely used assumption of elastic iris deformation and indicate that further research is required to develop a more realistic description of the behavior of iris structure upon dilation.

Rotation-Independent IRIS Matching by Motion Estimation

In this paper we propose a novel method of applying motion estimation techniques to human authentication by iris matching. By exploiting the inherent differences in vector fields generated by comparing same-class and different-class irises, good matching performance was obtained. The method was applied to 600 images of 150 eyes from the Bath database. The best settings of several parameters were determined through experimental minimization of equal error rate (EER), which was estimated from the matching and nearest non matching distributions. The effect of iris rotation was studied through circular shifts and seen to have minimal effects on match/non match scores. The standard deviation of the X-vector data was found to give best performance with 100% correct recognition rate (CRR) and a flat receiver operating characteristics (ROC) indicating no false accepts or rejects within the data with an estimated EER of 0.007. Images compressed with JPEG2000 at 0.5 bpp were similarly processed resulting in an EER of 0.014 at a normalized image size of 1536 bytes.

Correction to "DCT-Based Iris Recognition" [Apr 07 586-595]

In the above titled paper (ibid., vol. 29, no. 4, pp. 586-595, Apr 07), there was a transcription error in the definition of the DCT (Discrete cosine transform). The correct definition is provided here: http://csdl.computer.org/comp/trans/tp/2007/09/tp02.pdf