Elsevier

Pattern Recognition Letters

Volume 82, Part 2, 15 October 2016, Pages 251-257
Pattern Recognition Letters

Using iris and sclera for detection and classification of contact lenses

https://doi.org/10.1016/j.patrec.2015.10.009Get rights and content

Highlights

  • Detecting contact lenses is required for reliable iris-based authentication.

  • Iris and sclera information are both relevant for detection and classification.

  • A reliable segmentation method is devised to extract iris and sclera.

  • A bag-of-feature method based on dense descriptors is proposed.

  • Classification performance improves significantly w.r.t. previous state-of-the-art.

Abstract

Detecting the presence of contact lenses and their type helps increasing the reliability of iris-based authentication systems. We propose a machine-learning approach for this task, based on expressive local image descriptors. The image is first segmented to extract the iris and sclera regions, then scale-invariant local descriptors (SID) are computed densely on both areas, and summarized through the Bag-of-Features paradigm. Classification is based on a properly trained linear SVM. The major contributions of our proposal concern the segmentation algorithm, the use of information drawn from the sclera, and the use of non-rectified data to preserve local structures. A number of variants of the proposed method are investigated, working on different areas of the image, with alternative local descriptors, and with different encoding techniques. Eventually, results are compared with the state-of-the-art in the field. The experimental analysis, carried out on several publicly available datasets, shows that the proposed classification method based on a dense scale invariant descriptor outperforms all the reference techniques.

Introduction

Iris-based biometric authentication systems are becoming extremely popular thanks to their high reliability in both identification and verification tasks [5], [11], [27]. This is essentially due to the properties of the iris pattern, which is unique even for identical twins, and so rich of distinctive features that a casual wrong identification is seldom observed. Moreover, as an internal organ of the eye, the iris is well protected from the environment and stable with age [10].

The performance of iris recognition systems, however, can sharply degrade when the users wear contact lenses. The impairment is quite significant in the case of cosmetic lenses, used to modify the appearance of the iris, so as to alter its color and possibly also its texture [9]. However, it may be non-negligible also for transparent lenses, as recently shown in [2], due to induced artifacts. For example, the lens generally moves around on the eye’s surface, causing several artifacts that prevent a correct matching and increase the false rejection rate in the identification task. Hence, to improve iris recognition, it is important that the system be able to recognize whether the user wears contact lenses, and of which type, cosmetic or transparent.

Some examples of such images coming from the IIIT-D contact lens database [21] are shown in Fig. 1. It can be seen that cosmetic lenses have, typically, a very marked texture, more visible than that of natural irises, suggesting that they can be easily recognized based on the analysis of the iris region. Indeed, the iris with its complex structure, provides abundant textural information to exploit for classification purposes. On the contrary, transparent lenses, and in particular soft lenses, do not alter the characteristics of the iris. However, unlike in the no-lens case, a boundary of circular shape is always present, more or less visible, in the sclera region. We argue that these differences can be well captured by a fine-scale statistical analysis conducted over small patches of the images, provided that also part of the sclera region is examined.

In particular, in this work we use a Bag-of-Features paradigm where dense local features are extracted from all patches of the region of interest and jointly quantized for the classification task. This approach has proven very promising for detecting the biometric spoofing of different traits [15]. The regions of interest are extracted by means of an ad hoc segmentation algorithm. We take into account information coming both from the iris and from part of the sclera region, in order to detect both textured and transparent lenses. Moreover, in order to prevent any type of texture distortion, we avoid any normalization.

Preliminary results of this work were presented in [14]. With respect to that paper, the current version is largely improved under several points of view: (i) new versions of the proposed algorithm have been devised and analyzed; (ii) a more thorough assessment of the segmentation algorithm has been carried out, through the inclusion of new specific datasets; and (iii) the comparative assessment of classification results, conducted on the same datasets used in [48], includes a further technique very recently published. Such new data provide a better insight into the effectiveness of the proposed method.

The rest of the paper is organized as follows. In Section 2 the current literature on this topic is reviewed. Then, in Section 3, our approach is described in detail, while the experimental results are reported in Section 3.1. Eventually, Section 4 draws some conclusions.

Section snippets

Related work

Most of the research on this topic focuses on colored lenses, since they can be also used by a malicious user to attack the system. Fakes made by cosmetic lenses are the most challenging to detect, since, differently from printed-iris attacks [39], the alteration is localized in a very small part of the image.

A pioneering work in this direction is due to [10] where irises are detected by means of the periodicities left by the dot-matrix printers used in the fabrication process. Another method,

Proposed approach

In this section we describe the proposed method, which comprises three major steps: segmentation of the iris and part of the sclera region, dense feature extraction and classification by means of the Bag-of-Features paradigm.

Experimental results

To assess the classification performance of the proposed method a number of experiments were performed on the publicly available datasets used in [48] . Since these are thoroughly described in the reference paper, we report here only the major characteristics of interest for our experiments. The images of the NotreDame-I and II datasets, as already said, are provided with the associated ideal segmentation of the iris, while this is not available for the IIIT-D Cogent, and IIIT-D Vista datasets.

Conclusions

In this work, we propose a novel method to detect and classify cosmetic and soft contact lenses. A preliminary parametric segmentation of the image allows us to accurately identify the iris region. However, we also include part of the sclera in the region under analysis, since this area turns out to be very discriminative when dealing with transparent lenses. To perform classification we use the Bag-of-Features paradigm with features associated to the rotation and scale-invariant local

References (49)

  • J. Daugman

    High confidence visual recognition of persons by a test of statistical independence

    IEEE Trans. Pattern Anal. Mach. Intell.

    (1993)
  • J. Daugman

    Demodulation by complex-valued wavelets for stochastic pattern recognition

    Int. J. Wavel. Multiresolut. Inf. Process.

    (2003)
  • J. Daugman

    New methods in iris recognition

    IEEE Trans. Syst. Man Cybern. Part B

    (2007)
  • G. Erdogan et al.

    Automatic detection of non-cosmetic soft contact lenses in ocular images

    SPIE Biometric and Surveillance Technology for Human and Activity Identification

    (2013)
  • L. Ghiani et al.

    Fingerprint liveness detection using binarized statistical image features

    Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems

    (2013)
  • D. Gragnaniello et al.

    Contact lens detection and classification in iris images through scale invariant descriptor

    Proceedings of the Workshop on Insight on Eye Biometrics in Conjunction with SITIS

    (2014)
  • D. Gragnaniello et al.

    An investigation of local descriptors for biometrics spoofing detection

    IEEE Trans. Inf. Forensics Secur.

    (2015)
  • Z. He et al.

    Efficient iris spoof detection via boosted local binary patterns

    Advances in Biometrics

    (2009)
  • H. Hofbauer et al.

    A ground truth for iris segmentation

    Proceedings of the International Conference on Pattern Recognition

    (2014)
  • K. Hughes et al.

    Detection of contact-lens-based iris biometric spoofs using stereo imaging

    Proceedings of the Hawaii International Conference on System Sciences

    (2013)
  • H. Jégou et al.

    Aggregating local descriptors into a compact image representation

    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    (2010)
  • J. Kannala et al.

    Bsif: Binarized statistical image features

    Proceedings of the International Conference on Pattern Recognition

    (2012)
  • N. Kohli et al.

    Revisiting iris recognition with color cosmetic contact lenses

    Proceedings of the International Conference on Biometrics

    (2013)
  • I. Kokkinos, M. Bronstein, A. Yuille, Dense Scale Invariant Descriptors for Images and Surface, 2012. Research Report...
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    This paper has been recommended for acceptance by Maria De Marsico and Maria Frucci.

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