The 1st Competition on Counter Measures to Finger Vein Spoofing Attacks

Tomé, Pedro; Raghavendra, R.; Busch, Christoph; Tirunagari, Santosh; Poh, Norman; Shekar, B. H.; Gragnaniello, Diego; Sansone, Carlo; Verdoliva, Luisa; Marcel, Sébastien

doi:10.1109/icb.2015.7139067

Cited by 55 publications

(40 citation statements)

References 17 publications

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“…Table 1 and 2 indicates the quantitative performance of the proposed method when compared to 4 different stateof-the-art schemes that was employed in 1st Competition on Counter Measures to Finger Vein Spoofing Attacks [9]. Since the state-of-the-art schemes are based on frame based feature extraction and learning, we have used the training set (see Section 4.1) that comprised of first 50 unique instances with video frames to train the SVM classifier.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, we generated the artefacts by printing the real sample using different kinds of printers such as inkjet and laser. The motivation for using the print attack is by because it is very easy to generate, cost effective and already proven to be efficient in a previous study [9]. In order to generate the finger vein artefacts using inkjet printer, we first perform the pre-processing on each of the captured frame of the video by extracting the Region of Extract (ROI) and then rescaling the ROI finger vein to have dimension of 100 × 300 pixels.…”

Section: Real Sample Capturementioning

confidence: 99%

“…More recently, the 1st Competition on Counter Measures to Finger Vein Spoofing Attacks was organised in conjunction with the International Conference of Biometrics 2015. Figure 1 illustrates the finger vein artefact sample from the Spoofing-Attack finger vein database [9] used in this competition. Early results available from this competition introduced three different Presentation Attack Detection (PAD) algorithms using (1) Binarized Statistical Image Features (BSIF), (2) Riesz transform and (3) Local Phase Quantization (LPQ) [14] and Weber Local Descriptor (WLD).…”

Section: Introductionmentioning

confidence: 99%

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Finger vein liveness detection using motion magnification

Ramachandra

Avinash²,

Marcel

et al. 2015

2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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Finger vein recognition has emerged as an accurate and reliable biometric modality that was deployed in various security applications. However, the use of finger vein recognition also indicated its vulnerability to presentation attacks (or direct attacks). In this work, we present a novel algorithm to identify the liveness of the finger vein characteristic that is presented to the sensor. The core idea of the proposed approach is to magnify the blood flow through the finger vein to measure its liveness. To this extent, we employ the Eulerian Video Magnification (EVM) approach to enhancing the motion of the blood in the recorded finger vein video. Next, we further process the magnified video to extract the motion-based features using optical flow to identify the finger vein artefacts. Extensive experiments are carried out on a relatively large database that is comprised of 300 unique finger vein videos corresponding to 100 subjects. The finger vein artefact database is captured by printing the real (or normal) presentation image of the finger vein on a high-quality paper using two different kinds of printers namely laser and inkjet. Extensive comparative evaluation with four different well-established state-of-the-art schemes demonstrated the efficacy of the proposed scheme.

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Section: Resultsmentioning

confidence: 99%

Section: Real Sample Capturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finger vein liveness detection using motion magnification

Ramachandra

Avinash²,

Marcel

et al. 2015

2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)

Self Cite

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“…In biometric systems, the direct attack or presentation attack is of high interest because of the security evaluation in biometric systems. Finger vein recognition previously had a good spoofing resistance as compared to other biometric modalities, but recent literature has shown that FVR devices are vulnerable to spoof attack [129,130]. In 2007, Matsumoto deceive a system using a synthetic artifact, which was the first attempt to spoof a finger vein image to the best of our knowledge [12].…”

Section: Spoofing Attack (Presentation Attack) In Finger Vein Recognimentioning

confidence: 99%

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

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“…Among past biometrics-oriented challenges, events focusing on fingerprints [31], [32], [33] and facial images [3], [22], [41], [44] have likely been the most visible. Recent years have seen challenges and competitions on other modalities and biometric sub-problems ranging from iris [4], speaker [20], [34], sclera [9], finger-vein [52], [55], or keystroke dynamics [36] recognition to spoof detection [5], [7], [49], liveness detection [19], [35], [53], segmentation [9], [10] and others.…”

Section: Related Workmentioning

confidence: 99%

The unconstrained ear recognition challenge

Emeršič

Štepec

Štruc

et al. 2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions. The goal of the challenge was to assess the performance of existing ear recognition techniques on a challenging large-scale dataset and identify open problems that need to be addressed in the future. Five groups from three continents participated in the challenge and contributed six ear recognition techniques for the evaluation, while multiple baselines were made available for the challenge by the UERC organizers. A comprehensive analysis was conducted with all participating approaches addressing essential research questions pertaining to the sensitivity of the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer of the UERC was found to ensure robust performance on a smaller part of the dataset (with 180 subjects) regardless of image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3, 704 subjects was used for testing.

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The 1st Competition on Counter Measures to Finger Vein Spoofing Attacks

Abstract: Abstract

Cited by 55 publications

References 17 publications

Finger vein liveness detection using motion magnification

Finger vein liveness detection using motion magnification

A Systematic Review of Finger Vein Recognition Techniques

The unconstrained ear recognition challenge

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