Reference Threshold Calculation for Biometric Authentication

Malik, Jyoti; Girdhar, Dhiraj; Dahiya, Ratna; Sainarayanan, G.

doi:10.5815/ijigsp.2014.02.06

Cited by 35 publications

(17 citation statements)

References 23 publications

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“…False Rejection Rate (FRR) is the probability that the system incorrectly rejects access to an authorized person as the criteria of reference threshold is not fulfilled [9]. This may be due to the failure in finding a match between live image samples to the data derived from the FBS as part of 100-candidate matching samples (see Figure 2).…”

Section: Transactions With Hit and No Hit Resultsmentioning

confidence: 99%

“…This is an important process to assess if the resulting decision from FBS is a "genuine individual" type of decision or an "impostor" type of decision [8]. As the verification in the matching process are largely quantifiable, statistical calculation of various performance metrics were used which include False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (EER) as the results would demonstrate reliability and efficiency of the FBS [9]. The description of each statistical calculation together with its findings will be discussed in the following sections.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Facial Recognition in Multimodal Biometrics System for Finger Disabled Applicants

Mazlan

Harun

Suliman

2017

IJEECS

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show abstract

Section: Transactions With Hit and No Hit Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Facial Recognition in Multimodal Biometrics System for Finger Disabled Applicants

Mazlan

Harun

Suliman

2017

IJEECS

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“…The EER is the most broadly utilized in ASR for the performance measure. In biometric authentication system reference threshold, FAR, FRR goes hand in hand [22].Along these lines, the ASR performance of the Fuzzy modelling system was likewise measured utilizing the EER. Since the EER must be ascertained for a fixed number of code vectors, a codebook containing 256 codeword's was utilized to describe the performance of ASR between FCM, FVQ2, and NFVQ.…”

Section: System Evaluationmentioning

confidence: 99%

Efficient Modelling Technique based Speaker Recognition under Limited Speech Data

Singh¹,

Kumar²,

Kolluri³

2016

IJIGSP

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As on date, Speaker-specific feature extraction and modelling techniques has been designed in automatic speaker recognition (ASR) for a sufficient amount of speech data. Once the speech data is limited the ASR performance degraded drastically. ASR system for limited speech data is always a highly challenging task due to a short utterance. The main goal of ASR to form a judgment for an incoming speaker to the system as being which member of registered speakers. This paper presents a comparison of three different modelling techniques of speaker specific extracted information (i) Fuzzy c-means (FCM) (ii) Fuzzy Vector Quantization2 (FVQ2) and (iii) Novel Fuzzy Vector Quantization (NFVQ). Using these three modelling techniques, we developed a text independent automatic speaker recognition system that is computationally modest and equipped for recognizing a non-cooperative speaker. In this investigation, the speaker recognition efficiency is compared to less than 2 sec of text-independent test and train utterances of Texas Instruments and Massachusetts Institute of Technology (TIMIT) and self-collected database. The efficiency of ASR has been improved by 1% with the baseline by hiding the outliers and assigns them by their closest codebook vectors the efficiency of proposed modelling techniques is 98.8%, 98.1% respectively for TIMIT and self-collected database.

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“…Since the CFAR circuits automatically adjust the detection threshold, they have been enthusiastically used in radar systems. As a consequence, much attention has been paid to the task of designing and assessing these adaptive detection techniques since the task of keeping a near-constant probability of false alarm (CFAR) is of primary concern in modern radar systems [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers

Mashade¹

2014

IJIGSP

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This paper addresses the problem of detecting the partially-correlated  2 fluctuating targets with two and four degrees of freedom. It presents the performance analysis, in its exact form, of GTM-CFAR processor when the operating environment is contaminated with extraneous targets and the radar receiver post-detection integrates M pulses of exponentially correlated targets. Mathematical formulas for the detection and false alarm probabilities are derived, in the absence as well as in the presence of spurious targets which are fluctuating in accordance with the so-called moderately fluctuating  2 targets. A thorough performance assessment by several numerical examples, which has considered the role that each parameter can play in the processor performance, is also given. The results show that the processor performance improves, for weak SNR of the primary target, as the correlation coefficient ρ s increases and this occurs either in the absence or in the presence of outlying targets. As the strength of the target return increases, the processor tends to invert this behavior. The SWI & SWII and SWIII & SWIV models enclose the correlated target cases when the target correlation follows  2 fluctuation models with two and four degrees of freedom, respectively, and this behavior is common for all GTM based detectors.

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Reference Threshold Calculation for Biometric Authentication

Cited by 35 publications

References 23 publications

Facial Recognition in Multimodal Biometrics System for Finger Disabled Applicants

Facial Recognition in Multimodal Biometrics System for Finger Disabled Applicants

Efficient Modelling Technique based Speaker Recognition under Limited Speech Data

Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers

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