Biometric Security and Performance Metrics: FAR, FER, CER, FRR

Sivaram, M.; A, Mohamed Uvaze Ahamed.; Yuvaraj, D.; Megala, G.; Porkodi, V.; Kandasamy, Manivel

doi:10.1109/iccike47802.2019.9004275

Cited by 7 publications

(6 citation statements)

References 2 publications

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“…Basically, this is the error rate at which FRR equals FAR. FRRs and FERs are used to determine biometric accuracy and the system's capability of allowing limited access to authorized users [28]. The measures can differ widely depending on how sensitive the mechanism is set up to match the biometric.…”

Section: Biometric Performance Measuresmentioning

confidence: 99%

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Evaluation of User Perception on Biometric Fingerprint System

Yeboah¹,

Adewopo²,

Azumah³

et al. 2022

Preprint

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Biometric systems involve security assurance to make our system highly secured and robust. Nowadays, biometric technology has been fixed into new systems with the aim of enforcing strong privacy and security. Several innovative system have been introduced, and most of them have biometrics installed to protect military bases, banking machines, and other sophisticated systems, such as online tracking systems. Businesses can now focus on their core functions and feel confident about their data security. Despite the benefits and enhancements in security that biometrics offer, there are also some vulnerabilities. This study aimed to investigate the biometric vulnerabilities in a healthcare facility and propose possible countermeasures for biometric system vulnerabilities.

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Section: Biometric Performance Measuresmentioning

confidence: 99%

“…In addition to reducing false-acceptance rates, false-rejection rates can also be increased. This makes it essential to understand how vendors calculate FARs and FRRs [13,28]. As a result of their interdependence, plotting them against each other provides meaningful insights.…”

Section: Biometric Performance Measuresmentioning

confidence: 99%

Evaluation of User Perception on Biometric Fingerprint System

Yeboah¹,

Adewopo²,

Azumah³

et al. 2022

Preprint

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“…A study of performance metrices for biometric security is carried out in Sivaram et al (2019). Daugman (2004), presented how iris recognition system works and a comparison is made among 9.1 million users acquired in trails in Britain the USA, Korea and Japan.…”

Section: Related Workmentioning

confidence: 99%

Noisy iris smoothing and segmentation scheme based on improved Wildes method

Kumawat

Panda

2022

Multidim Syst Sign Process

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In an automated iris recognition system, in order to get higher accuracy, we should have an efficient iris segmentation process. The reliability of accurate “iris recognition” system largely depends on the accuracy of segmentation process. Traditional “iris segmentation” methods are unable to detect the exact boundaries of iris and pupil, which is time consuming and also highly sensitive to noise. To overcome these problems, we have proposed an improved Wildes method (IWM) for segmentation in iris recognition system. The proposed algorithm consists of two major steps before applying Wildes method for segmentation: edge detection of iris and pupil from a noisy eye image with improved Canny with fuzzy logic (ICWFL) and removal of unwanted noise from above step with a hybrid restoration fusion filter (HRFF). A comparative study of various edge detection techniques is performed to prove the efficiency of ICWFL method. Similarly, the proposed method is tested with various noise densities from 10 to 95 dB. Also the working of the proposed HRFF is compared with some existing smoothing filters. Various experiments have been performed with the help of iris database of IIT_Delhi. Both visual and numerical results prove the efficiency of the proposed algorithm.

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“…5 summarizes the results in terms of False Acceptance Rate (FAR) and False Rejection Rate (FRR), where (i) the FAR is the ratio of the approved number of imposters to the total number of submitted forgeries (i.e., what percentage of times the system accepts for an invalid person), and (ii) The FRR is the ratio of the number of genuine test signatures rejected by the system to the total number of genuine test signatures submitted (i.e., the percentage of times a valid user is rejected by the system). These two metrics (i.e., FAR and FRR) are used for evaluating many handwritten signature recognition systems [54]. From Tabs.…”

Section: Nrvs Vs Frvsmentioning

confidence: 99%

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Hefny¹,

Hassanien²,

Basha³

2021

Computers, Materials &Amp; Continua

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Identity verification using authenticity evaluation of handwritten signatures is an important issue. There have been several approaches for the verification of signatures using dynamics of the signing process. Most of these approaches extract only global characteristics. With the aim of capturing both dynamic global and local features, this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system (NRVS) and Genetic NRVS (GNRVS) models. The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values: truth, indeterminacy, and falsity. These three values are determined by neutrosophic membership functions. The proposed model also is able to deal with all features without the need to select from them. In the GNRVS model, the neutrosophic rules are automatically chosen by Genetic Algorithms. The performance of the proposed system is tested on the MCYT-Signature-100 dataset. In terms of the accuracy, average error rate, false acceptance rate, and false rejection rate, the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.

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Biometric Security and Performance Metrics: FAR, FER, CER, FRR

Cited by 7 publications

References 2 publications

Evaluation of User Perception on Biometric Fingerprint System

Evaluation of User Perception on Biometric Fingerprint System

Noisy iris smoothing and segmentation scheme based on improved Wildes method

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

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