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Proceedings ArticleDOI

Fingerphoto spoofing in mobile devices: A preliminary study

01 Sep 2016-pp 1-7

TL;DR: This research is aimed at understanding the effect of spoofing on fingerphoto spoofing, and creating a large spoofed fingerphoto database and making it publicly available for research.

AbstractBiometric-based authentication for smart handheld devices promises to provide a reliable and alternate security mechanism compared to traditional methods such as pins, patterns, and passwords. Although fingerprints are a viable source for authentication, they generally require installation of an additional hardware such as optical and swipe sensors on mobile devices, and are only available in expensive, high-end smartphones. Alternatively, fingerphoto images captured using the smartphone camera for authentication is one of the promising biometric approaches. However, using fingerphotos for authentication brings along a major challenge of fingerphoto spoofing. This research is aimed at understanding the effect of spoofing on fingerphotos. There are three major contributions of this research: (i) create a large spoofed fingerphoto database and make it publicly available for research, (ii) to establish the effect of print attack and photo attack in fingerphoto spoofing, and (iii) understand the performance of existing spoofing detection algorithms on fingerphoto spoofing.

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Citations
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Book ChapterDOI
01 Jan 2019
TL;DR: This chapter focuses on how fingerprint technology can be used to improve speed and accuracy of certain processes, i.e. exams as the society accepts this as part of everyday life as well as in an educational setting where youngsters are much used to digital technologies.
Abstract: Fingerprint technology has evolved immensely since its initial use in the 1800s when it was used solely to assist with crime investigations. It is now used as a convenience replacing passwords and PIN numbers from logging into bank accounts, mobile devices, gaining access into rooms and various other processes where time plays a key factor. This chapter focuses on how fingerprint technology can be used to improve speed and accuracy of certain processes, i.e. exams as the society accepts this as part of everyday life. In particular, we look at a use case in an educational setting where youngsters are much used to digital technologies as part of their daily life.

41 citations

Book ChapterDOI
22 Sep 2019
TL;DR: This chapter introduces the topic of selfie biometrics and aims to improve the understanding and advance the state-of-the-art in this field.
Abstract: Traditional password-based solutions are being predominantly replaced by biometric technology for mobile user authentication. Since the inception of smartphones, smartphone cameras have made substantial progress in image resolution, aperture size, and sensor size. These advances facilitate the use of selfie biometrics such as the self-acquired face, fingerphoto, and ocular region for mobile user authentication. This chapter introduces the topic of selfie biometrics to the readers. Overview of the methods for different selfie biometrics modalities is provided. Liveness detection, soft-biometrics prediction, and cloud-based infrastructure for selfie biometrics are also discussed. Open issues and research directions are included to provide the path forward. The overall aim is to improve the understanding and advance the state-of-the-art in this field.

9 citations

Proceedings ArticleDOI
01 Jan 2019
TL;DR: The experimental results suggest that the features extracted from the high frequency band carries significant discriminatory information for replay attack detection, and the subband analysis on constant-Q cepstral coefficient (CQCC) and mel-frequency cepstal coefficient (MFCC) features to improve the performance of Replay attack detection.
Abstract: Automatic speaker verification systems have been widely employed in a variety of commercial applications. However, advancements in the field of speech technology have equipped the attackers with sophisticated techniques for circumventing speaker verification systems. The state-of-the-art countermeasures are fairly successful in detecting speech synthesis and voice conversion attacks. However, the problem of replay attack detection has not received much attention from the researchers. In this study, we perform subband analysis on constant-Q cepstral coefficient (CQCC) and mel-frequency cepstral coefficient (MFCC) features to improve the performance of replay attack detection. We have performed experiments on the ASVspoof 2017 database which consists of 3566 genuine and 15380 replay utterances. Our experimental results suggest that the features extracted from the high frequency band carries significant discriminatory information for replay attack detection. In particular, our approach achieves an improvement of 36.33% over the baseline replay attack detection method in terms of equal error rate.

7 citations


Cites background from "Fingerphoto spoofing in mobile devi..."

  • ...However, the attacks at the sensor-level, also known as presentation attacks, can be carried out successfully with utmost ease, as shown for several biometric modalities including face [4], iris [5] and fingerprint [6]....

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Posted Content
TL;DR: The results show that fingerphotos are promising to authenticate individuals (against a national ID database) for banking, welfare distribution, and healthcare applications in developing countries.
Abstract: We address the problem of comparing fingerphotos, fingerprint images from a commodity smartphone camera, with the corresponding legacy slap contact-based fingerprint images. Development of robust versions of these technologies would enable the use of the billions of standard Android phones as biometric readers through a simple software download, dramatically lowering the cost and complexity of deployment relative to using a separate fingerprint reader. Two fingerphoto apps running on Android phones and an optical slap reader were utilized for fingerprint collection of 309 subjects who primarily work as construction workers, farmers, and domestic helpers. Experimental results show that a True Accept Rate (TAR) of 95.79 at a False Accept Rate (FAR) of 0.1% can be achieved in matching fingerphotos to slaps (two thumbs and two index fingers) using a COTS fingerprint matcher. By comparison, a baseline TAR of 98.55% at 0.1% FAR is achieved when matching fingerprint images from two different contact-based optical readers. We also report the usability of the two smartphone apps, in terms of failure to acquire rate and fingerprint acquisition time. Our results show that fingerphotos are promising to authenticate individuals (against a national ID database) for banking, welfare distribution, and healthcare applications in developing countries.

7 citations


Cites background from "Fingerphoto spoofing in mobile devi..."

  • ...[12] have explored the fingerprint anti-spoofing techniques for smartphone based authentication....

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Journal ArticleDOI
Abstract: Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade Through a touchless acquisition process, many issues of touch-based systems are circumvented, eg, the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface However, touchless fingerprint recognition systems reveal new challenges In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks Also, further issues, eg, interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups Many works have been proposed so far to put touchless fingerprint recognition into practice Published approaches range from self identification scenarios with commodity devices, eg, smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenariosThis work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges An overview of available research resources completes the work

7 citations


References
More filters
Journal ArticleDOI
01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

58,232 citations


"Fingerphoto spoofing in mobile devi..." refers methods in this paper

  • ...Three different matching approaches are adopted: (i) L2 distance based matching, (ii) Neural Network (NN), and (iii) Random Decision Forest (RDF) [6]....

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  • ...We see that the photo attack with iPad-Nokia has the least TAR for ScatNet+NN and ScatNet+RDF matching algorithms, and it is in accordance with Table 3 which provides the highest EER for iPad-Nokia....

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  • ...ScatNet + RDF yields the best results for both spoofed and nonspoofed images; the EERs are in the range of 0.48% to 2.53%....

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Journal ArticleDOI
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

37,868 citations


"Fingerphoto spoofing in mobile devi..." refers methods in this paper

  • ...In general, those images that are not correctly matched with the matching algorithm are easily distinguished as spoof images by the SVM classifier, which is in accordance with the basic understanding of the problem....

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  • ...Hence, spoof detection is formulated as a binary classification problem using an SVM [7] to learn these texture patterns from a spoofed image....

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  • ...To study the behavior of high definition display devices such as retina display, gradient based DSIFT [10] features and LUCID descriptor [17] are also independently used to learn an SVM. LUCID descriptors are recently found to provide successful performance in the domain of mobile biometric liveness detection [5]....

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  • ...While using the complete test set, LBP + SVM gives the best spoofed fingerphoto detection performance with 3.71% EER when the complete spoofed dataset is considered....

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  • ...We evaluate the performance of different features such as Local Binary Patterns (LBP), Dense Scale Invariant Feature Transform (DSIFT), and Locally Uniform Comparison Image Descriptor (LUCID) features along with Support Vector Machine (SVM) based fingerphoto spoofing detection algorithm to distinguish between spoofed and non-spoofed images....

    [...]

Proceedings ArticleDOI
20 Sep 1999
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

15,597 citations


"Fingerphoto spoofing in mobile devi..." refers methods in this paper

  • ...To study the behavior of high definition display devices such as retina display, gradient based DSIFT [10] features and LUCID descriptor [17] are also independently used to learn an SVM. LUCID descriptors are recently found to provide successful performance in the domain of mobile biometric liveness detection [5]....

    [...]

  • ...To study the behavior of high definition display devices such as retina display, gradient based DSIFT [10] features and LUCID descriptor [17] are also independently used to learn an SVM....

    [...]

  • ...We evaluate the performance of different features such as Local Binary Patterns (LBP), Dense Scale Invariant Feature Transform (DSIFT), and Locally Uniform Comparison Image Descriptor (LUCID) features along with Support Vector Machine (SVM) based fingerphoto spoofing detection algorithm to distinguish between spoofed and non-spoofed images....

    [...]

  • ...The results using both LBP, DSIFT, and LUCID descriptors are presented in Table 5 and Table 6 and the ROC curves are shown in Figure 5....

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  • ...Further, we evaluated different features such as LBP, DSIFT, and LUCID combined with a learning algorithm to classify spoofed and original images....

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Journal ArticleDOI
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

5,237 citations


"Fingerphoto spoofing in mobile devi..." refers methods in this paper

  • ...The texture patterns are extracted using LBP features [3, 8, 11]....

    [...]

Proceedings ArticleDOI
TL;DR: This work presents a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print, and analyzes the texture of the facial images using multi-scale local binary patterns (LBP).
Abstract: Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture features. Hence, we present a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyzes the texture of the facial images using multi-scale local binary patterns (LBP). Compared to many previous works, our proposed approach is robust, computationally fast and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on a publicly available database showed excellent results compared to existing works.

530 citations


"Fingerphoto spoofing in mobile devi..." refers methods in this paper

  • ...The texture patterns are extracted using LBP features [3, 8, 11]....

    [...]