Human Identification Using Selected Features From Finger Geometric Profiles

Bera, Asish; Bhattacharjee, Debotosh

doi:10.1109/tsmc.2017.2744669

Cited by 25 publications

(23 citation statements)

References 34 publications

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“…In the field of hand biometrics, geometrical and local features are extracted using generic or customized feature extractors, such as Radon transform, Gabor filter [20], scale invariant feature transform (SIFT) extractor [6], customized shape, geometrical, or figure feature extractors [4,29]. The extracted features are used to drive the final decision, which is taken based on either a similarity metric or by an off-the-shelf classifier.…”

Section: Hand-based Biometric Identification Methodsmentioning

confidence: 99%

11K Hands: Gender recognition and biometric identification using a large dataset of hand images

Afifi

2019

Multimed Tools Appl

113

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The human hand possesses distinctive features which can reveal gender information. In addition, the hand is considered one of the primary biometric traits used to identify a person. In this work, we propose a large dataset of human hand images (dorsal and palmar sides) with detailed ground-truth information for gender recognition and biometric identification. Using this dataset, a convolutional neural network (CNN) can be trained effectively for the gender recognition task. Based on this, we design a two-stream CNN to tackle the gender recognition problem. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for the biometric identification task. We show that the dorsal side of hand images, captured by a regular digital camera, convey effective distinctive features similar to, if not better, those available in the palmar hand images. To facilitate access to the proposed dataset and replication of our experiments, the dataset, trained CNN models, and Matlab source code are available at (https://goo.gl/rQJndd).

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Section: Hand-based Biometric Identification Methodsmentioning

confidence: 99%

11K Hands: Gender recognition and biometric identification using a large dataset of hand images

Afifi

2019

Multimed Tools Appl

113

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“…The result for the IITD database was EER=4.6%. [9] proposed a method which enables classification of a hand contour with the use of a "k-nearest neighbours" (kNN) classifier, which was obtained from [35]. These authors also used a classifier called "Random Forest".…”

Section: Selected Methods Of Persons Identification With Use Of Hand mentioning

confidence: 99%

Hand contour classification using evolutionary algorithm

Moravec¹

2020

ITC

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A biometric identification of persons wchich utilize contour of a human hand belogs to still very interesting and still not totally explored areas and its accuracy and effectiveness depends on technical capabilities to some extent. Presented paper solves given problem using combination of different algorithms. A hand contour is used, topological description of the hand, evolutionary algorithm, algorithm linear regression to estimate the knuckles positions and for contours comparison is used an algorithm Iterative Closest Point (ICP) in its genuine shape. All 5 fingers is at computer classification fully moveable, thumb has 2 knuckles. Modern evolutionary optimizers enable markedly to cut down computational demands of the algorithm ICP. Experimental verification of proposed recipes were performed with use of two different databases named THID and GPDS with persons of both gender and different age (cca 20-65let) with total number of oeprons in individual database 104 and 94. Experimental results checked succesfuly suitability of use combination of methods ICP and evolutionary optimizer which is named as EPSDE for solving of the given task with algorithmic complexity O(N) and success rate give by coefficient THID:EER=0.38% and GPDS:EER=0.35% on real images.

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“…According to [7], Bosporus is another finger vein recognition dataset. In the preparation stage, before finger recognition, the entire binary hand image is hand normalized to generate the left finger contour and the right finger contour.…”

Section: Finger Vein Recognition Finger Vein Recognition Has the Advantages Of Security And Privacymentioning

confidence: 99%

Impact on Biometric Identification Systems of COVID-19

Guo

2021

Scientific Programming

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COVID-19 has had an inevitable impact on the daily life of people in 2020. Changes in behavior such as wearing masks have a considerable impact on biometric systems, especially face recognition systems. When people are aware of this impact, a comprehensive evaluation of this phenomenon is lacking. The purpose of this paper is to qualitatively evaluate the impact of COVID-19 on various biometric systems and to quantitatively evaluate face detection and recognition. The experimental results show that a real-world masked face dataset is essential to build an effective face recognition-based biometric system.

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Human Identification Using Selected Features From Finger Geometric Profiles

Cited by 25 publications

References 34 publications

11K Hands: Gender recognition and biometric identification using a large dataset of hand images

11K Hands: Gender recognition and biometric identification using a large dataset of hand images

Hand contour classification using evolutionary algorithm

Impact on Biometric Identification Systems of COVID-19

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