Feature Fusion for Fingerprint Liveness Detection: a Comparative Study

Toosi, Amirhosein; Bottino, A.; Cumani, Sandro; Negri, Pablo; Sottile, Pietro Luca

doi:10.1109/access.2017.2763419

Cited by 33 publications

(10 citation statements)

References 51 publications

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“…Deep fusion. Toosi et al proposed in [60] a completely different approach to use DL for fingerprint PAD. Instead of using deep networks for feature extraction, ten different hand-crafted descriptors, including the well-known local phase quantization (LPQ), binary statistical features (BSIF) or scale invariant feature transform (SIFT) were fed to a self-developed deep network (Spidernet) for final fusion and classification.…”

Section: B Deep Learning For Conventional Sensorsmentioning

confidence: 99%

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

Tolosana

Gomez‐Barrero

Busch

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, Presentation Attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. The biometrics community has thus made a considerable effort to the development of automatic Presentation Attack Detection (PAD) mechanisms, for instance through the international LivDet competitions.In this context, we present a novel fingerprint PAD scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-the-art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.36% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).

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Section: B Deep Learning For Conventional Sensorsmentioning

confidence: 99%

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

Tolosana

Gomez‐Barrero

Busch

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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show abstract

“…Description 2012 Marasco et al [12] Different frameworks for integrating a spoof detection module with a recognition system 2015 Wen et al [154] Ensemble of SVMs on reflection, blurriness, chromatic moment, and color diversity 2015 Raghavendra et al [155] Feature level concatenation with Light Field Camera based features 2015 Arashloo et al [156] Fused MBSIF-TOP and MLPQ-TOP using SR-KDA 2016 Ding et al [89] Bayesian Belief Networks for fusing match scores with liveness scores 2016 Boulkenafet et al [157] CoALBP and LPQ features in HSV and YCbCr colour space 2016 Patel et al [158] Concatenation of color moments and LBP features 2016 Siddiqui et al [159] Inter-feature and intra-feature score-level fusion of multi-scale LBP and HOOF features 2016 Ding and Ross [160] Fusion of multiple one-class SVMs to improve generalizability of a fingerprint spoof detector 2017 Toosi et al [161] Comparative study of different fusion techniques on ten fingerprint features 2017 Korshunov and Marcel [162] Studies impact of score fusion on presentation attack detection for voice 2018 Komeili et al [163] Fusion of ECG recognition and fingerprint spoof detection 2018 Yadav et al [164] Fusion of (VGG features+PCA) with (RDWT+Haralick) features and neural network 2018 Sajjad et al [165] Two-tier authentication system for recognition and spoof detection 2018 Chugh et al [166] CNN based spoof detection on fingerprint patches fused a CNN with RNN in order to extract pseudo-depth images and a remote photoplethysmography (RPPG) signal from an input face video. The extracted information were then fused for face anti-spoofing.…”

Section: Year Authorsmentioning

confidence: 99%

“…Recently, Toosi et al [161] presented a comparative study with ten feature descriptors for the task of fingerprint spoof detection. The authors experimented with different fusion strategies to achieve improved performance.…”

Section: Year Authorsmentioning

confidence: 99%

A comprehensive overview of biometric fusion

2019

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The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of singlebiometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.

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“…A local binary pattern over a Gaussian pyramid is also examined as a liveness feature [11]. Toosi et al performed a comprehensive analysis on different liveness features and studied the effectiveness of different feature fusion methods [12].…”

Section: Related Workmentioning

confidence: 99%

Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network

2019

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Fingerprints are known to be easily synthesized to trick identification systems. In this paper, we propose a new method that incorporates template fingerprints stored for identification in the liveness detection system. The fingerprint identification platform must have a list of template fingerprints stored for matching with new probe fingerprints trying to access the system. Thus, instead of simply detecting the liveness of the probe fingerprints, the proposed approach uses the matching template fingerprints along with probe fingerprints through convolutional neural networks to make the liveness decision, which comprises two sequential convolutional neural networks for classification. The proposed method can be built on the top of existing liveness detection methods to increase accuracy without a significant increase in computation time. The evaluation over the LivDet dataset shows that the proposed fingerprint liveness detection method is able to obtain state-of-the-art accuracy.

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Feature Fusion for Fingerprint Liveness Detection: a Comparative Study

Cited by 33 publications

References 51 publications

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

A comprehensive overview of biometric fusion

Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network

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