Effect of Severe Image Compression on Iris Recognition Performance

Abstract

We investigate three schemes for severe compression of iris images, in order to assess what their impact would be on recognition performance of the algorithms deployed today for identifying persons by this biometric feature. Currently, standard iris images are 600 times larger than the IrisCode templates computed from them for database storage and search; but it is administratively desired that iris data should be stored, transmitted, and embedded in media in the form of images rather than as templates computed with proprietary algorithms. To reconcile that goal with its implications for bandwidth and storage, we present schemes that combine region-of-interest isolation with JPEG and JPEG2000 compression at severe levels, and we test them using a publicly available government database of iris images. We show that it is possible to compress iris images to as little as 2 KB with minimal impact on recognition performance. Only some 2% to 3% of the bits in the IrisCode templates are changed by such severe image compression. Standard performance metrics such as error trade-off curves document very good recognition performance despite this reduction in data size by a net factor of 150, approaching a convergence of image data size and template size.

Custom JPEG Quantization for Improved Iris Recognition Accuracy

“…In previous work [9,7], we have compared five general purpose compression algorithms (including JPEG and JPEG2000) with respect to their impact on iris recognition accuracy of three different recognition schemes (the CASIA database has been used). In accordance to [3] superior compression performance of JPEG2000 over JPEG is found especially for low bitrates, however, for high and medium quality JPEG is still an option to consider. So far, compression algorithms have been applied to iris imagery with their respective standard settings.…”

“…Rakshit and Monro [11] again use JPEG2000 to compress polar iris images up to a compression rate of 80 and studies the impact on verification accuracy of three iris recognition systems (including the Daugman algorithm, the CASIA database is used). Daugman and Downing [3] apply JPEG and JPEG2000 to rectilinear image data (the NIST ICE database is used) and remove image background (i.e. parts of the image not being part of the eye like eye-lids are replaced by constant average gray) before compression is applied.…”

Evolutionary optimization of JPEG quantization tables for compressing iris polar images in iris recognition systems

“…We apply a 1-D implementation of the Daugman iris recognition algorithm, provided by Libor Masek 1 . The utilized polar images are extracted from the picture set of the CASIA V1.0 database (Chinese Academy of Sciences -Institute of Automation Version 1.0, short: CASIA 2 V1.0).…”

“…As lately investigated in [2], Ives presents first results and techniques on image compression and the resulting change of iris recognition performance. Furthermore, in [1] and [5] it is stated that JPEG2000 has a better performance than JPEG when compressing iris images and especially for low bit rates. Otherwise, JPEG requires less computational demand and is competitive to JPEG2000 at higher image qualities.…”

A New Phase-Correlation-Based Iris Matching for Degraded Images

“…To handle non ideal iris images, J. Daugman proposes a quality measure calculated as a function of several measures including percentage of visible iris, blur measures using the Fourier transform and the sharpness of the pupil boundary and interlaced raster shear detection [16].…”