Person Authentication Based on Biometric Traits Using Machine Learning Techniques

Kumar, Gagan; Chowdhury, Debbrota Paul; Bakshi, Sambit; Kumar, Pankaj

doi:10.1201/9781003054115-8

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Compared to more precise bio-metric techniques, facial recognition has an added advantage: it does not require approval, cooperation or even knowledge of the person concerned. [26], [32] A. Use of Technology Governments are leading the list of customers of this technology, which amounts to about $ 4 billion, namely government agencies that deal with collective identification systems such as prisons, airports, vehicle licensing authorities, and, of course, police.…”

Section: Introductionmentioning

confidence: 99%

Crime Detection and Suspect Identification System

Hosameldeen¹,

Alhammadi²,

SALEHI³

2023

Preprint

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—The number of ATMs in various countries is increasing steadily and rapidly with the number of users increasing very widely. On the other hand, banks have become more interested in finding the best procedures to combat ATM crimes to ensure the safety and security of their customers and other cardholders. This has become an excellent target for some criminals or fraudsters, despite the limited amounts that can be withdrawn from these devices, given a maximum daily limit. We aim at implementing this system inside bank ATMs in order to detect objects like guns, hammers, and knives. Once the suspicious objects and actions are detected, we perform facial recognition to identify whether the suspect is a repeating offender. We use object, face, and action recognition algorithms to achieve our objective. Results showed that using our proposed algorithm is efficient in detecting threatening objects

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Section: Introductionmentioning

confidence: 99%

Crime Detection and Suspect Identification System

Hosameldeen¹,

Alhammadi²,

SALEHI³

2023

Preprint

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show abstract

“…If Local Binary Pattern (LBP) descriptors are used straight away, review illustrates that, prove to be analysis will become more effective for face image. Since this method was utilized for face representation [23], there has been a growing interest in LBP-based features for other representations also [25].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Performance Analysis of Feature Extraction Approach: Local Binary Pattern and Principal Component Analysis for Iris Recognition system

Divya¹,

Rajendra²

2022

IJEER

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Many techniques have been proposed for the recognition of Iris. Most of them are single resolution techniques which results in poor performance. In this paper, feature extraction approaches like local binary pattern and principal component analysis assimilation has been offered. For classification, Support Vector Machine has been used. This paper compares the efficiency of two popular feature extraction methods Principal Component Analysis and Local Binary Pattern using two different iris databases CASIA and UBIRIS. The models were tested using 200 iris images. Statistical parameters like F1 score and Accuracy are tested for different threshold values. Our proposed method results with accuracy of 94 and 92%, is obtained for using Local Binary Pattern for CASIA and UBIRIS data set respectively. The Receiver Operating Characteristic Curve has been drawn and Area under Curve is also calculated. The experiment has been extended by varying the dataset sizes. The result shows that LBP achieves better performance with both CASIA and UBIRIS databases compared to PCA.

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Person Authentication Based on Biometric Traits Using Machine Learning Techniques

Cited by 2 publications

References 1 publication

Crime Detection and Suspect Identification System

Crime Detection and Suspect Identification System

Performance Analysis of Feature Extraction Approach: Local Binary Pattern and Principal Component Analysis for Iris Recognition system

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