The Use of Spatial Distribution of the Local Histogram Based Features for Finger's Veins Biometrics

Fadhil, Ruaa; George, Loay E.

doi:10.9734/bjmcs/2016/24729

Cited by 1 publication

(1 citation statement)

References 15 publications

(9 reference statements)

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“…where, A is the number of successful authentications by impostors, B is the number of attempts at authentication by unauthorized users, C is the number of failed attempts at authentication by authorized users, and D is the number of attempts at authentication by genuine users. Furthermore accuracy parameter can be used to evaluate the performance of biometric systems (i.e., the proportion of correct predictions) and it does not need to take into consideration what is positive (P) and what is negative (N) [25], [26]. 𝐴𝐶𝐶 = 𝑇𝑃+𝑇𝑁 𝑃+𝑁 (6) where true positive (TP) is the number of genuine users that identified correctly, true negative (TN) is the number of impostor users attempts that rejected by the system.…”

Section: Verification (Authentication) Resultsmentioning

confidence: 99%

An ear recognition system based on local wavelet subband energy distribution

Fadhil¹,

George

2021

IJEECS

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The outer ear features have been used for many years in forensic science of recognition. Human ear is a valuable information provenance of data for individual identification/authentication. Ear meets biometric characteristic (universality, distinctiveness, permanence and collectability). Biometric system depending on ear image facing two major challenges; the first one is the localization of human ear area in given profile face image, and the second one is the selection of proper features to distinguish between individuals. In this work, we propose an alogorithm for ear recognition based on the local spatial energy distribution of wavelet sub-bands, because of wavelet transform has the ability to analyze the local feature of 2-D image by determining where the low frequency and high frequency areas are and it provides full description of the spatial distribution of the ear image. Nearest classifier are used to make a recognition decision in matching stage. The system was tested over a public database consist of 493 images. The attained recognition rate was (95.28%) and the achieved minimum equal error rate (EER) is 0.02%.

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Section: Verification (Authentication) Resultsmentioning

confidence: 99%