On an Empirical Study: Face Recognition using Machine Learning and Deep Learning Techniques
Authors: 
Chahreddine Medjahed, Freha Mezzoudj, Abdellatif Rahmoun, Christophe CharrierAuthors Info & Claims
Chahreddine Medjahed, Freha Mezzoudj, Abdellatif Rahmoun, Christophe CharrierAuthors Info & ClaimsArticle No.: 13, Pages 1 - 9
Abstract
Face recognition is an interesting topic in biometrics research, which can be divided into two sub-problems: face detection followed by face recognition. The application of face recognition in real life situations and pose variations still remains a challenge. The aim of this paper is to evaluate and compare various systems of face recognition based on speed and high accuracy Machine Learning algorithms. The Support Vectors Machine is a strong algorithm for mutli-classification. The feed- forward Neural Network is a popular one. Recently, Deep Learning is becoming a very important subset of machine learning due to its high level of performance across many types of data, in particular using Convolutional Neural Networks (CNNs). A large colored Face Database is used to evaluate these three proposed and adapted architectures. The results are competitive.
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- On an Empirical Study: Face Recognition using Machine Learning and Deep Learning Techniques
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Published: 22 March 2021
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ICIST '20
ICIST '20: 10th International Conference on Information Systems and Technologies
June 4 - 5, 2020
Lecce, Italy
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