A hybrid partial fingerprint matching algorithm for estimation of Equal error rate

Agrawal, Pinki; Kapoor, Ravikant; Agrawal, Sanjay

doi:10.1109/icaccct.2014.7019308

Cited by 13 publications

(10 citation statements)

References 8 publications

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“…In evaluating SER performance, two metrics were used, equal-error-rate (EER) and normal classification measurement. The EER metric [64] , [65] , [66] uses a threshold value to predict false acceptance and rejection rates. If the false acceptance and rejection rates are equal, it implies that the percentage of those two rates is balanced, which is called the equal error rate.…”

Section: Methodsmentioning

confidence: 99%

Vector learning representation for generalized speech emotion recognition

Singkul

Woraratpanya

2022

Heliyon

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Section: Methodsmentioning

confidence: 99%

Vector learning representation for generalized speech emotion recognition

Singkul

Woraratpanya

2022

Heliyon

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“…Some matching strategies use the coordinates and number of pores in conjunction to minutiae features [49][50][51]. There are also methods designed for matching partial fingerprint images [2,3,34], rolled acquisitions [50], or latent fingerprint impressions [49].…”

Section: Extraction Of Pores From Fingerprint Imagesmentioning

confidence: 99%

“…Recent studies proved that Level 3 features can greatly increase the accuracy of current fingerprint recognition technologies [49][50][51]. In particular, the number and coordinates of the sweat pores demonstrated to be highly discriminative features [19,53], also in the case of partial fingerprints [2,3,34]. These features are particularly suitable for recognition methods dealing with touch-based and touchless samples because the pores are visible in most of the regions of the fingerprint images and their analysis can increase the accuracy of systems with high security requirements such as automated border controls [8].…”

Section: Introductionmentioning

confidence: 99%

A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

Labati

Genovese

Muñoz

et al. 2018

Pattern Recognition Letters

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Most fingerprint recognition systems use Level 1 characteristics (ridge flow, orientation, and frequency) and Level 2 features (minutiae points) to recognize individuals. Level 3 features (sweat pores, incipient ridges and ultra-thin characteristics of the ridges) are less frequently adopted because they can be extracted only from high resolution images, but they have the potential of improving all the steps of the biometric recognition process. In particular, sweat pores can be used for quality assessment, liveness detection, biometric matching in live applications, and matching of partial latent fingerprints in forensic applications. Currently, each type of fingerprint acquisition technique (touch-based, touchless, or latent) requires a different algorithm for pore extraction. In this paper, we propose the first method in the literature able to extract the coordinates of the pores from touch-based, touchless, and latent fingerprint images. Our method uses specifically designed and trained Convolutional Neural Networks (CNN) to estimate and refine the centroid of each pore. Results show that our method is feasible and achieved satisfactory accuracy for all the types of evaluated images, with a better performance with respect to the compared state-of-the-art methods.

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“…This technique involves, binarization, thinning using Block filter, minutiae matching and computing matching scores. Agarwal et al [16] presented a method of estimation of equal error rate based on false acceptance rate and false rejection rate to increase accuracy of partial finger print matching. In this algorithm weak descriptors and pores based local binary pattern method was used.…”

Section: Related Workmentioning

confidence: 99%

Fingerprint Verification in Personal Identification by Applying Local Walsh Hadamard Transform and Gabor Coefficients

Pushpalatha¹,

Gautam²

2017

IJIVP

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In an era of advanced computer technology world where innumerable services such as access to bank accounts, or access to secured data or entry to some national important organizations require authentication of genuine individual. Among all biometric personal identification systems, fingerprint recognition system is most accurate and economical technology. In this paper we have proposed fingerprint recognition system using Local Walsh Hadamard Transform (LWHT) with Phase Magnitude Histograms (PMHs) for feature extraction.

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A hybrid partial fingerprint matching algorithm for estimation of Equal error rate

Cited by 13 publications

References 8 publications

Vector learning representation for generalized speech emotion recognition

Vector learning representation for generalized speech emotion recognition

A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

Fingerprint Verification in Personal Identification by Applying Local Walsh Hadamard Transform and Gabor Coefficients

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