Fingerphoto spoofing in mobile devices: A preliminary study

doi:10.1109/BTAS.2016.7791201

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Fingerphoto spoofing in mobile devices: A preliminary study

Proceedings Article•DOI•

Fingerphoto spoofing in mobile devices: A preliminary study

Archit Taneja, Aakriti Tayal, Aakarsh Malhorta, Anush Sankaran, Mayank Vatsa, Rieha Singh - Show less +2 more

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.

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Abstract: Biometric-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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Book Chapter•DOI•

Introduction to Selfie Biometrics

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Ajita Rattani¹, Reza Derakhshani², Arun Ross³•Institutions (3)

Wichita State University¹, University of Missouri–Kansas City², Michigan State University³

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.

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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.

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10 citations

Proceedings Article•DOI•

Subband Analysis for Performance Improvement of Replay Attack Detection in Speaker Verification Systems

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Sachin Garg¹, Shruti Bhilare¹, Vivek Kanhangad¹•Institutions (1)

Indian Institute of Technology Indore¹

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.

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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.

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9 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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Proceedings Article•DOI•

On Privacy Preserving Anonymization of Finger-Selfies

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Aakarsh Malhotra¹, Saheb Chhabra¹, Mayank Vatsa², Richa Singh²•Institutions (2)

Indraprastha Institute of Information Technology¹, Indian Institute of Technology, Jodhpur²

14 Jun 2020

TL;DR: It is demonstrated that this could potentially lead to matching finger-selfies with livescan fingerprints, and a new publicly available Social-Media Posted Finger-selfie (SMPF) Database, containing 1,000 finger- selfie images posted on Instagram is proposed.

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Abstract: With the availability of smartphone cameras, high speed internet, and connectivity to social media, users post content on the go including check-ins, text, and images. Privacy leaks due to posts related to check-ins and text is an issue in itself, however, this paper discusses the potential leak of one’s biometric information via images posted on social media. While posting photos of themselves or highlighting miniature objects, users end up posting content that leads to an irreversible loss of biometric information such as ocular region, fingerprint, knuckle print, and ear print. In this paper, we discuss the effect of the loss of the finger-selfie details from social media. We demonstrate that this could potentially lead to matching finger-selfies with livescan fingerprints. Further, to prevent the leak of the finger-selfie details, we propose privacy preserving adversarial learning algorithm. The algorithm learns a perturbation to prevent the misuse of finger-selfie towards recognition, yet keeping the visual quality intact to highlight the minuscule object. The experiments are presented on the ISPFDv1 database. Further, we propose a new publicly available Social-Media Posted Finger-selfie (SMPF) Database, containing 1,000 finger-selfie images posted on Instagram.

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7 citations

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

...Similarly, access to ridge-valley details from latent impression of the surface of smartphones [6, 15] or other sources can be used to generate 3D silicone fingerprints [20] (or presentation attack) to spoof the recognition system [21, 26]....
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Proceedings Article•DOI•

Face/Fingerphoto Spoof Detection under Noisy Conditions by using Deep Convolutional Neural Network

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Masakazu Fujio¹, Yosuke Kaga¹, Takao Murakami², Tetsushi Ohki³, Kenta Takahashi¹ - Show less +1 more•Institutions (3)

Hitachi¹, National Institute of Advanced Industrial Science and Technology², Shizuoka University³

01 Jan 2018

TL;DR: This research first investigates local texture based anti-spoofing methods including existing popular methods (but changing some of the parameters) by using publicly available spoofed face/finger photo/video databases, and investigates the spoof detection under the camera defocus or hand movements during image capturing.

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Abstract: Most of the generic camera based biometrics systems, such as face recognition systems, are vulnerable to print/photo attacks. Spoof detection, which is to discriminate between live biometric information and attacks, has received increasing attentions recently. However, almost all the previous studies have not concerned the influence of the image distortion caused by the camera defocus or hand movements during image capturing. In this research, we first investigate local texture based anti-spoofing methods including existing popular methods (but changing some of the parameters) by using publicly available spoofed face/finger photo/video databases. Secondly, we investigate the spoof detection under the camera defocus or hand movements during image capturing. To simulate image distortion caused by camera defocus or hand movements, we create blurred test images by applying image filters (Gaussian blur or motion blur filters) to the test datasets. Our experimental results demonstrate that modifications of the existing methods (LBP, LPQ, DCNN) or the parameter tuning can achieve less than 1/10 of HTER(half total error rate)compared to the existing results. Among the investigated methods, the DCNN (AlexNet) can achieve the stable accuracy under the increasing intensity of the blurring noises.

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5 citations

Proceedings Article•DOI•

Presentation Attack Detection for Smartphone Based Fingerphoto Recognition Using Second Order Local Structures

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Pankaj Wasnik¹, Raghavendra Ramachandra¹, Kiran B. Raja¹, Christoph Busch¹•Institutions (1)

Norwegian University of Science and Technology¹

01 Nov 2018

TL;DR: This work proposes a robust presentation attack detection scheme based on the features extracted from the maximum response images obtained from the convolution of second order Gaussian derivatives and the input images at multiple scales, which has achieved the detection performance of BPCER.

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Abstract: Fingerprint recognition on smartphone provides a good alternative over traditional security measures such as lock-patterns and pin. However, such fingerprint systems have some inherent problems such as the fact that user will leave their latent fingerprint on the sensor, and the limited sensor area. Additionally, fingerprint sensors impact on the cost and form factor of the device. Hence, in literature, the camera based approaches such as fingerphoto recognition systems got the attention of many researchers and manufacturers. However, such systems are highly vulnerable to presentation attacks such as photo-prints, display and replay attacks. To countermeasure these attacks, we propose a robust presentation attack detection scheme based on the features extracted from the maximum response images obtained from the convolution of second order Gaussian derivatives and the input images at multiple scales. The proposed scheme has achieved the detection performance of BPCER of 1.8%, 0.0% and 0.66% at APCER=10% for the presentation attack instrument species i.e., print-photo, display and replay attacks respectively.

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4 citations

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

...Furthermore, in [13], authors have proposed a PAD for fingerphoto recognition using SVM and texture and gradient-based features for print-photo and display attacks where they have achieved an EER of 3....
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...could be used to fool the biometric system [12], [13]....
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...Further, we observe that the proposed scheme shows an improved PAD performance when compared to the state-of-art methods [12], [13], where authors have achieved...
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References

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Open Access

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Journal Article•DOI•

Random Forests

[...]

Leo Breiman¹•Institutions (1)

University of California, Berkeley¹

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.

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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.

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79,257 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 Article•DOI•

LIBSVM: A library for support vector machines

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Chih-Chung Chang¹, Chih-Jen Lin¹•Institutions (1)

National Taiwan University¹

06 May 2011-ACM Transactions on Intelligent Systems and Technology

TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

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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.

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40,826 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....
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Proceedings Article•DOI•

Object recognition from local scale-invariant features

[...]

David G. Lowe¹•Institutions (1)

University of British Columbia¹

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.

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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.

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16,989 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....
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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....
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...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 Article•DOI•

Face Description with Local Binary Patterns: Application to Face Recognition

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Timo Ahonen¹, Abdenour Hadid¹, Matti Pietikäinen¹•Institutions (1)

University of Oulu¹

01 Dec 2006-IEEE Transactions on Pattern Analysis and Machine Intelligence

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.

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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

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5,563 citations

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

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

Face spoofing detection from single images using micro-texture analysis

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Jukka Maatta¹, Abdenour Hadid¹, Matti Pietikäinen¹•Institutions (1)

University of Oulu¹

11 Oct 2011-International Journal of Central Banking

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).

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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.

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628 citations