A novel finger vein feature extraction technique for authentication

Rajan, R. Sundara; Indu, M. G.

doi:10.1109/aicera.2014.6908263

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…In 2014, Rajan and Indu [ 97 ] used a Fast Retina Keypoint (FREAK) descriptor to extract features from finger vein images. The keypoints are found by first applying a Frangi filter to the finger vein image, and the Features from Accelerated Segment Test (FAST) algorithm is then used to find the keypoints.…”

Section: Finger Vein Feature Extractionmentioning

confidence: 99%

Feature Extraction for Finger-Vein-Based Identity Recognition

et al. 2021

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This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

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Section: Finger Vein Feature Extractionmentioning

confidence: 99%

Feature Extraction for Finger-Vein-Based Identity Recognition

et al. 2021

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“…Finger images include noise with rotational and translational variations. To eliminate these variations, it is subjected to pre-processing steps that include image filtering and enhancement b S and histogram equalization h(μ); see [39,45,55] for more details.…”

Section: Finger-vein Image Pre-processingmentioning

confidence: 99%

Feature extraction of finger-vein patterns based on boosting evolutionary algorithm and its application for loT identity and access management

Darwish

2021

Multimed Tools Appl

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With billions of devices being connected, how to make sure that our information stays secure is becoming a hot topic of IoT. Traditional approaches to personal authentication are inadequate and ineffective in the IoT era. Finger vein technology is the current biometric system that utilizes the vein structure for recognition. As such patterns are veiled under the skin surface, they have significant privacy protection and are therefore incredibly difficult to forge. Finger vein recognition has gained a great deal of publicity because earlier approaches experienced significant pitfalls, such as its inability to handle the imbalanced collection of finger veins samples and the detection of distinguishing features in low-quality images. Such disadvantages have triggered a lack of consistency of the optimization algorithm or have contributed to a decrease in its efficiency. The key objective of the research discussed in this paper is to examine the impact of the genetic algorithm in the selection of the optimum vector characteristics of the finger vein. This is done by incorporating a Niching model in the form of a Context-Based Clearing (CBC) procedure to increase the heterogeneity of the features within the features' vector, with the goal of minimizing the association between them. It also offers the idea of a reduction of the feature set to reduce duplication without reducing accuracy. The performance study of the proposed model is carried out through multiple tests and the findings indicate an overall increase of 6% in the accuracy relative to some of the state-of-the-art finger vein recognition systems present in the literature.

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“…Intheirpaper(K. Wang,Ma,P.,&Liu,2012)proposedanotherframeworkforcoordinating fingerveinpicturesutilizingrelativedistanceandangles.Themeetingpointswereextractedfrom thefingerveinimagebyenlistingnumberofarmsstartingfromapixel.Bythenthesepointswere connectedwitheachotherwithstraightlinestoshapeameshtopology.Thenthedistancebetween thepointsandtheanglebetweenthemwascalculated.Inanycase,therelativedistanceandangle computationfromameshtopologyisdifficulttoperform. (Rajan & Indu, 2014) proposed a Novel Finger Vein Feature Extraction Technique for Authentication,thismethodutilizesamixofFrangifilterFASTalgorithmandFREAKdescriptors.…”

Section: Related Workmentioning

confidence: 99%

Discriminant Analysis and Naïve Bayes Classifier-Based Biometric Identification Using Finger Veins

Qayoom

Naaz

2019

International Journal of Computer Vision and Image Processing

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Finger vein identification is a dominating method of biometric technology used for authentication in a highly secure environment. Vein patterns are unique for each individual and it is underneath skin so there is less chance for forgery. In the current research work, finger vein features are extracted and verified for the purpose of authentication. The first step in this work is to pre-process the image obtained from the database. In order to get the region of interest (ROI) the threshold value is calculated using a standard deviation method followed by morphology-based functions available in the MATLAB software. After pre -processing a Gabor filter, fast filter, and freak descriptors are used. The features calculated at the freak descriptor processing are trained on classifiers namely discriminant and Naïve Bayes. The features trained to the classifiers are then fed again into the classifiers and cross verified to update the results of accuracy. The accuracy calculated using discriminant analysis is 94.46% and by using Naïve Bayes is 98.38%.

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A novel finger vein feature extraction technique for authentication

Cited by 11 publications

References 6 publications

Feature Extraction for Finger-Vein-Based Identity Recognition

Feature Extraction for Finger-Vein-Based Identity Recognition

Feature extraction of finger-vein patterns based on boosting evolutionary algorithm and its application for loT identity and access management

Discriminant Analysis and Naïve Bayes Classifier-Based Biometric Identification Using Finger Veins

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