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Brno University of Technology, Czech Republic

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National Taiwan University of Science and Technology, Taiwan, Province of China

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Institute of Medical Technology and Equipment, Poland

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Hongik University, Korea

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Ca’ Foscari University of Venice, Italy

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Ton Duc Thang University, Vietnam

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VSB - Technical University of Ostrava, Czech Republic

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Ton Duc Thang University, Vietnam

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Wroclaw University of Science and Technology, Poland

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The University of Edinburgh, United Kingdom

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Guanajuato University, Mexico

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Technical University of Cluj Napoca, Romania

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Posts and Telecommunications Institute of Technology, Ho Chi Minh City, Vietnam

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Henan Polytechnic University, China

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Ton Duc Thang University, Vietnam

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University of Pardubice, Czech Republic

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Delhi Technological University, India

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Ho Chi Minh City University of Technology and Education, Vietnam

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DIMES Department of University of Calabria, Italy

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Henan Polytechnic University, China

Nguyen Quang Sang
Ho Chi Minh City University of Transport, Vietnam

Anh-Tu Le
Ho Chi Minh City University of Transport, Vietnam

Phu Tran Tin
Ton Duc Thang University, Vietnam


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Different Approaches for Face Authentication as Part of a Multimodal Biometrics System

Jaromir Tovarek, Miroslav Voznak, Jan Rozhon, Filip Rezac, Jakub Safarik, Pavol Partila

DOI: 10.15598/aeee.v16i1.2547


Abstract

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.

Keywords


Face authentication; HOG; LBP; MLNN; SVM.

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