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A Survey of Finger Vein Recognition
2014As a new biometric technique, finger vein recognition has attracted lots of attentions and efforts from researchers, and achieved some progress in recent years. A survey of progress in finger vein recognition is given in this paper. It mainly focuses on three aspects, i.e., the general introduction of finger vein recognition, a review of the existing ...
Lu Yang 0005 +3 more
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Efficient Finger Vein Localization and Recognition
2010 20th International Conference on Pattern Recognition, 2010In order to achieve accurate recognition of human finger vein (FV), this paper addresses the problems of finger vein localization and vein feature extraction. An inherent physical property of human fingers is used to localize the region of interest (ROI) of vein images as well as removing uninformative vein imagery based on the inter-phalangeal joint ...
Jinfeng Yang, Xu Li
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Rotation Invariant Finger Vein Recognition
2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2019Finger vein recognition deals with the identification of subjects based on its venous pattern within the fingers. The majority of the scanner devices capture a single finger from the palmar side using light transmission. Some of them are equipped with a contact surface or other structures to support in finger placement.
Bernhard Prommegger, Andreas Uhl
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Cancelable biometrics for finger vein recognition
2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016Cancelable biometrics is one of the possible solutions to security and privacy problems in biometrics-based recognition systems. In this paper we propose the use of two classical transformations, block re-mapping and image warping, for the definition of cancelable biometrics from finger vein pattern images.
Emanuela Piciucco +4 more
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Finger vein recognition based on finger crease location
Journal of Electronic Imaging, 2016Finger vein recognition technology has significant advantages over other methods in terms of accuracy, uniqueness, and stability, and it has wide promising applications in the field of biometric recognition. We propose using finger creases to locate and extract an object region.
Zhiying Lu, Shumeng Ding, Jing Yin
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Rotation Invariant Finger Vein Recognition
2012Finger vein patterns have recently been recognized as an effective biometric identifier and many related work can achieve satisfied results. However, these methods usually suppose the database is non-rotated or slightly rotated, which are strict for preprocessing stages, especially for capture.
Shaohua Pang +3 more
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A finger vein recognition system
2016 Conference on Advances in Signal Processing (CASP), 2016The most vital requirement in today's world of spoofing attacks is the high security. The development in consumer electronics demands for high security with high accuracy and high speed of authentication. Human behavioural and physiological features in biometrics has the large scope as a solution for security issues.
Manisha Sapkale, S. M. Rajbhoj
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A Novel Method for Finger Vein Recognition
2019Benefiting from CNN’s strong feature expression ability, the finger vein recognition systems using the convolutional neural network (CNN) currently have shown a good performance. However, these systems usually adopt such large networks or complex step-by-step processes that they cannot be applied to the hardware platform with limited computing power ...
Junying Zeng +6 more
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Local Vein Texton Learning for Finger Vein Recognition
2014In finger vein recognition, the input image is generally labeled in accordance with the nearest enrolled neighbor. However, it is so rigid that it is inadequate for some cases. This paper explores a modified sparse representation method for finger vein recognition.
Lu Yang 0005 +3 more
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Robustness of finger-vein recognition
2018One of the big issues in biometric recognition is robustness of recognition accuracy against sample signal quality degradation. The performance of a biometric recognition system is usually heavily affected by sample signal quality. A wide variety of factors potentially influence the quality of acquired biometric samples. The different types of features
Christof Kauba, Andreas Uhl
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