Different approaches for face authentication as part of a multimodal biometrics system
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Different approaches for face authentication as part of a multimodal biometrics system
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Advances in Electrical and Electronic Engineering
ISSN
1336-1376
e-ISSN
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Volume of the periodical
16
Issue of the periodical within the volume
1
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
7
Pages from-to
118-124
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85044927159