Different approaches for face authentication as part of a multimodal biometrics system

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10238881" target="_blank" >RIV/61989100:27240/18:10238881 - isvavai.cz</a>

Výsledek na webu

<a href="http://advances.utc.sk/index.php/AEEE/article/view/2547" target="_blank" >http://advances.utc.sk/index.php/AEEE/article/view/2547</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15598/aeee.v16i1.2547" target="_blank" >10.15598/aeee.v16i1.2547</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Different approaches for face authentication as part of a multimodal biometrics system

Popis výsledku v původním jazyce

This paper describes different approaches for the face authentication from the features and classification abilities point of view. Authors compare two types of features - Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) including their combination. These parameters are classified using Multilayer Neural Network (MLNN) and Support Vector Machines (SVM). Face authentication consists of several steps. The first step contains Viola-Jones algorithm for face detection. Authors resize the detected face for a fixed vector and afterwards, it is converted into grayscale. Next, feature extraction with a simple Min-Max normalization is applied. Obtained parameters are evaluated by classifiers and for each detected face, authors get posterior probability as the output of the classifier. Different approaches for face authentication are compared with each other using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) curves. The results are verified with AR Face Database and elaborated in a feature extraction and classifier design point of view. Best results were achieved by HOG feature for SVM classifier. Detailed results are listed in the text below. (C) 2018 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING.

Název v anglickém jazyce

Different approaches for face authentication as part of a multimodal biometrics system

Popis výsledku anglicky

This paper describes different approaches for the face authentication from the features and classification abilities point of view. Authors compare two types of features - Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) including their combination. These parameters are classified using Multilayer Neural Network (MLNN) and Support Vector Machines (SVM). Face authentication consists of several steps. The first step contains Viola-Jones algorithm for face detection. Authors resize the detected face for a fixed vector and afterwards, it is converted into grayscale. Next, feature extraction with a simple Min-Max normalization is applied. Obtained parameters are evaluated by classifiers and for each detected face, authors get posterior probability as the output of the classifier. Different approaches for face authentication are compared with each other using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) curves. The results are verified with AR Face Database and elaborated in a feature extraction and classifier design point of view. Best results were achieved by HOG feature for SVM classifier. Detailed results are listed in the text below. (C) 2018 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Advances in Electrical and Electronic Engineering

ISSN

1336-1376

e-ISSN

—

Svazek periodika

16

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

7

Strana od-do

118-124

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85044927159