Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future are outlined, and a short list of recommended readings is suggested.

Presentation Attack Detection for Iris Recognition: An Assessment of the State-of-the-Art

An approach for iris contact lens detection and classification using ensemble of customized DenseNet and SVM

Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future are outlined, and a short list of recommended readings are suggested.

Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art.

NIST Fingerprint ImageQuality (nfiq 2) is open source software that links image quality of optical and ink 500 pixel per inch fingerprints to operational recognition performance. This allows quality values to be tightly defined and then numerically calibrated, which in turn allows for the standardization needed to support a worldwide deployment of fingerprint sensors with universally interpretable image qualities. nfiq 2 quality features are formally standardized as part of ISO/IEC 29794-4 and serve as the reference implementation of the standard.

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NFIQ 2 NIST Fingerprint Image Quality

We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain. We require that the probe and gallery images be represented in terms of a fixed-length representation, which is typical for representations obtained from learned networks. Our encryption scheme is agnostic to how the fixed-length representation is obtained and can, therefore, be applied to any fixed-length representation in any application domain. Our method, dubbed HERS (Homomorphically Encrypted Representation Search), operates by (i) compressing the representation towards its estimated intrinsic dimensionality, (ii) encrypting the compressed representation using the proposed fully homomorphic encryption scheme, and (iii) searching against a gallery of encrypted representations directly in the encrypted domain, without decrypting them, and with minimal loss of accuracy. Numerical results on large galleries of face, fingerprint, and object datasets such as ImageNet show that, for the first time, accurate and fast image search within the encrypted domain is feasible at scale (296 seconds; 46x speedup over state-of-the-art for face search against a background of 1 million).

HERS: Homomorphically Encrypted Representation Search

With the advent of deep learning models, face recognition systems have achieved impressive recognition rates. The workhorses behind this success are Convolutional Neural Networks (CNNs) and the availability of large training datasets. However, we show that small human-imperceptible changes to face samples can evade most prevailing face recognition systems. Even more alarming is the fact that the same generator can be extended to other traits in the future. In this work, we present how such a generator can be trained and also extended to other biometric modalities, such as ﬁngerprint recognition systems.

Vishesh Mistry

Papers

AdvBiom: Adversarial Attacks on Biometric Matchers