The ICB-2015 Competition on Finger Vein Recognition

Xian, Ran; Ni, Lei; Li, Wenxin

doi:10.1109/icb.2015.7139080

Cited by 13 publications

(3 citation statements)

References 3 publications

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“…these can only be analysed in the context of a visit at the corresponding institutions, including GUC45 [81], GUC-FPFV-DB [225] and GUC-Dors-FV-DB [219] (where the former are palmar and the latter is a dorsal dataset, respectively). A special case is the (large-scale) datasets of Peking University, which are only partially available, but can be interfaced by the RATE 6 (Recognition Algorithm Test Engine), which has also been used in the series of (International) Finger Vein Recognition Contests (ICFVR/FVRC/PFVR) [281,282,303,312]. This series of contests demonstrated the advances made in this field, e.g.…”

Section: Hand-based Vascular Traitsmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

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Section: Hand-based Vascular Traitsmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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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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“…In recent years, new machine learning algorithms appeared and some of them have been introduced to the field of biometrics successfully. In order to encourage development of new algorithms for finger vein recognition, we held FVRC 2015 [12] and FVRC 2016 [13] competitions, jointly with ICB 2015 and 2016 respectively. This year, we continued to hold International Competition on Finger Vein Recognition (ICFVR) 2017 jointly with IJCB 2017.…”

Section: Introductionmentioning

confidence: 99%

ICFVR 2017: 3rd international competition on finger vein recognition

Zhang

Huang

Zhang

et al. 2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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In recent years, finger vein recognition has become an important sub-field in biometrics and been applied to realworld applications. The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets. In order to motivate research on finger vein recognition, we released the largest finger vein data set up to now and hold finger vein recognition competitions based on our data set every year. In 2017, International Competition on Finger Vein Recognition (ICFVR) is held jointly with IJCB 2017. 11 teams registered and 10 of them joined the final evaluation. The winner of this year dramatically improved the EER from 2.64% to 0.483% compared to the winner of last year. In this paper, we introduce the process and results of ICFVR 2017 and give insights on development of state-of-art finger vein recognition algorithms.

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The ICB-2015 Competition on Finger Vein Recognition

Cited by 13 publications

References 3 publications

State of the Art in Vascular Biometrics

State of the Art in Vascular Biometrics

The unconstrained ear recognition challenge

ICFVR 2017: 3rd international competition on finger vein recognition

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