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

Jaromir Tovarek,Jan Rozhon,Miroslav Voznak,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.

Highlights

Personal authentication can be divided into three fields according to methods used
As the testing data for imposters, digital images from 0.9 10 participants (10 imposters) were used. These participants do not belong to the reference users. This training process was re- 0.7 peated for Local Binary Patterns (LBP) only, Histogram of Oriented Gradients (HOG) only and LBP+HOG features
The aim of our research was to find the best features for face authentication and suitable classifier with the lowest values of False Acceptance Rate (FAR) and False Rejection Rate (FRR)

Summary

Introduction

Personal authentication can be divided into three fields according to methods used. A lot of work has been done in the last years in the field of face authentication as part of a multimodal biometrics system. Authors [6] used eigenface as features and GMM for classification in their research. Raghavendra et al [8] compared four methods for feature extraction (PCA, 2DPCA, LDA, 2DLDA). Each of these feature vectors was classified by nearest neighbour classifier. Barbu et al [12] used SIFT-based face recognition technique for feature extraction. Authors used measurement of the distance between feature vectors for classification. Chandrasheker et al [10] used SVM and HMM for classification and Xie et al [13] used only SVM

Objectives

Results

Conclusion