On Generation and Analysis of Synthetic Iris Images

Zuo, Jinyu; Schmid, Natalia A.; Chen, Xiaohan

doi:10.1109/tifs.2006.890305

Cited by 81 publications

(46 citation statements)

References 8 publications

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“…Recently, several researchers have addressed the problem of generating different synthetic biometric traits such as iris [11,12,13,14,15], fingerprints [16], signature [17,18], face [19], handwriting [20], and voice [21]. All these efforts have been mainly focused on the generation of new synthetic data, intended in general to overcome the limitation of assembling large biometric databases for performance assessment purposes.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms

Galbally

Ross

Gomez‐Barrero

et al. 2013

Computer Vision and Image Understanding

123

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractA binary iriscode is a very compact representation of an iris image. For a long time it was assumed that the iriscode did not contain enough information to allow for the reconstruction of the original iris. The present work proposes a novel probabilistic approach based on genetic algorithms to reconstruct iris images from binary templates and analyzes the similarity between the reconstructed synthetic iris image and the original one. The performance of the reconstruction technique is assessed by empirically estimating the probability of successfully matching the synthesized iris image against its true counterpart using a commercial matcher. The experimental results indicate that the reconstructed images look reasonably realistic. While a human expert may not be easily deceived by them, they can successfully deceive a commercial matcher. Furthermore, since the proposed methodology is able to synthesize multiple iris images from a single iriscode, it has other potential applications including privacy enhancement of iris-based systems.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms

Galbally

Ross

Gomez‐Barrero

et al. 2013

Computer Vision and Image Understanding

123

View full text Add to dashboard Cite

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“…Pioneering work in this direction was done by Terzopoulos and Waters [3] for facial images and sequences, by Cappelli et al [4] for fingerprints, and by Cui et al [5] and by Zuo et al [6] for iris images. In the future, such programs for generating particular biometric images might therefore serve as ways to "compress" them in Kolmogorov's sense; and one might even anticipate biometric recognition by comparison of the synthesizing programs.…”

Section: Introductionmentioning

confidence: 99%

Effect of Severe Image Compression on Iris Recognition Performance

Daugman

Downing

2008

IEEE Trans.Inform.Forensic Secur.

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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.

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“…A growing interest has arisen in the biometric community over the last decade for the generation of synthetic biometric traits such as voice [18], fingerprints [11], iris [80], handwriting [47], face [57] or signature [58].…”

Section: Related Workmentioning

confidence: 99%

A novel hand reconstruction approach and its application to vulnerability assessment

Gomez‐Barrero

Galbally

Morales

et al. 2014

Information Sciences

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in:Information Sciences 238 (2014) AbstractThe present work proposes a novel probabilistic method to reconstruct a hand shape image from its template. We analyse the degree of similarity between the reconstructed images and the original samples in order to determine whether the synthetic hands are able to deceive hand recognition systems. This analysis is made through the estimation of the success chances of an attack carried out with the synthetic samples against an independent system. The experimental results show that there is a high chance of breaking a hand recognition system using this approach. Furthermore, since it is a probabilistic method, several synthetic images can be generated from each original sample, which increases the success chances of the attack.

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On Generation and Analysis of Synthetic Iris Images

Cited by 81 publications

References 8 publications

Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms

Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms

Effect of Severe Image Compression on Iris Recognition Performance

A novel hand reconstruction approach and its application to vulnerability assessment

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