Finger Vein Recognition Algorithm Using Phase Only Correlation

Mahri, Nurhafizah; Suandi, Shahrel Azmin; Rosdi, Bakhtiar Affendi

doi:10.1109/etchb.2010.5559283

Cited by 44 publications

(26 citation statements)

References 8 publications

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“…Mahri et al [4] proposed a new technique by simply applying Phase Only Correlation function at the matching stage with a simple preprocessing technique.…”

Section: A Related Workmentioning

confidence: 99%

“…Biometrics has been widely used in forensics and in civilian applications such as crime detection, banking security, physical access control, information system security, national ID systems, and voter and driver registration [3]. Among all biometric techniques, finger vein recognition is considered as one of the most secure, reliable and accurate biometric because of its unique and distinct features [4,5]. Finger vein biometric is the technology of verifying people using their finger veins pattern based on the fact that every individual has different veins pattern [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Finger vein recognition using Discrete Wavelet Packet Transform based features

Shrikhande

Fadewar

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Finger vein biometric has become most promising recognition method due to its accuracy, reliability and security. This paper discusses a novel technique for finger veins features extraction using Discrete Wavelet Packet Transform (DWPT) based method. The DWPT without HH subband decomposition is applied on ROI of 96x64 size finger veins image up to third level. The average standard deviation and average energy of each decomposition level are used for the creation of features vector database. The Euclidean, City Block and Canberra distance classifiers are used for the classification of finger veins images. The performance of proposed method is evaluated on the standard finger veins image ROI database of SDUMLA Shandong University. Experimental results show that the proposed method gives better results as compare to the standard Discrete Wavelet Transform (DWT) and DWPT Methods.

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“…Mahri et al [4] proposed a new technique by simply applying Phase Only Correlation function at the matching stage with a simple preprocessing technique.…”

Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finger vein recognition using Discrete Wavelet Packet Transform based features

Shrikhande

Fadewar

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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“…Mahri et al in [17] presented an algorithm for vein recognition with less complexity in the image pre-processing phase, where vein pattern extraction is not included in the authentication process. Phase only correlation is applied at the matching stage.…”

Section: Iirelated Workmentioning

confidence: 99%

A Biometric System for Person Authentication Based on Finger Vein Patterns on FPGA

Jadhav¹

2015

IJAREEIE

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ABSTRACT:The personal verification is a critical key problem in today's society; also protection of personal information is very difficult because of different physical and software attacks. Due to this fact, the biometric authentication has gaining popularity as it provides a high security and reliable approach for personal authentication. Finger vein biometric is advantageous over other types of biometric systems because it has low forgery rate, aliveness detection as well as stable over long period of time. The paper presents the implementation of image processing algorithm and feature extraction processes in MATLAB, and for final authentication the template matching is carried out on Field Programmable Gate Array (FPGA) for fast recognition. The system is prototyped on Papilio one 500k FPGA board which has Xilinx Spartan 3E chip inside. The performance of the proposed system is evaluated by the time required to verify one input finger vein sample and Precision. KEYWORDS:Biometric, finger vein recognition, FPGA, MATLAB, Precision. I.INTRODUCTIONBiometrics is an automated recognition of an individual based on uniqueness of their biological or behavioural characteristics. The personal information can be protected in the form of biometrics. The traditional authentication systems like identity card or password can be easily stolen or acquired by unauthorized person [1]. All these traditional authentication systems are gradually replaced by biometric systems like fingerprints, iris recognition, palm print and veins. The biometric authentication system is chosen over conventional authentication system because of their distinctiveness and highly secured nature.Out of these biometric systems, finger vein biometric is one of the emerging techniques. In this type of biometric system the vascular pattern under one's skin is utilized as a unique feature for authentication. Veins are hidden underneath the skin surface and are mostly invisible to human eye, they are not prone to external distortion and also the vein patterns are much harder to replicate as compared to other biometric traits. Vein patterns are unique for each individual and are stable over a long period of time. Because of its uniqueness, stability and high resistance to criminal attacks, vein pattern is more reliable biological feature for a secure biometric authentication system [5][2].The Biometric system is often implemented in an untrusted environment that uses an insecure and non-reliable central server for the storage of the biometric templates [9]. This can be the source of biometric information leakage. The solution to this problems is given by recent development in vein authentication by hardware implementation of the system [12] [7][11] [18]. It provides secure information storage and tamper resistance, hence it provides the protection from physical and software attacks.The finger-vein biometric pattern for personal authentication is promising in security and convenience point of view. The comparison between different biometric systems is as show...

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“…As show in the table, taking into consideration of matching accuracy and information entropy, k is selected as 60, and the best EER=0.512%. To verify the validity of the proposed method, in this experiment, state-of-the-art methods for finger vein recognition such as LBP [6], (2D) 2 FPCA [12], BLPOC [7], Gabor [4], HOG [8]and HOL [11]are implemented for comparison. Table 2 shows the receiver operating characteristics (ROC) curves derived from the above methods.…”

Section: Advances In Intelligent Systems Research Volume 146mentioning

confidence: 99%

“…Many efforts are contributed to develop this technology and the technology has made remarkable progress with these efforts in the resent two decades. The representative methods include repeated line tracking by Miura [3], various feature extraction methods based on Gabor filter [4,5], local binary pattern (LBP)method based on local information [6], band-limited phase only correlation filter (BLPOC) [7], histogram of oriented gradients (HOG) descriptor [8], and various subspace learning methods, etc. These methods investigate various feature description for finger vein.…”

Section: Introductionmentioning

confidence: 99%

A Binary Code Learning Method Based on Class-neighborhood for Finger Vein Recognition

Zhang

Ning

et al. 2018

Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)

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Abstract-The finger vein recognition, as a new biometric identification technology, judges the identity of a person by using their finger vein angiography images obtained by near-infrared light, and has recently drawn considerable attention from scientists worldwide. In this work, a novel binary code learning method based on class-neighborhood (BCLCN) is proposed for finger vein recognition. Different from traditional binary code methods based on pixels, BCLCN fully considers the relationships between categories of train set and encodes based those relationships. In this way, the discrimination of the code is enhanced and the binary codes are shortened. Experimental results on self-built finger vein image database demonstrate the effectiveness of BCLCN (EER = 0.512%).

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Finger Vein Recognition Algorithm Using Phase Only Correlation

Cited by 44 publications

References 8 publications

Finger vein recognition using Discrete Wavelet Packet Transform based features

Finger vein recognition using Discrete Wavelet Packet Transform based features

A Biometric System for Person Authentication Based on Finger Vein Patterns on FPGA

A Binary Code Learning Method Based on Class-neighborhood for Finger Vein Recognition

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