Discriminant face features extraction, analysis & its application in multipose face recognization: a survey

Shekapure, S.S.; Kadam, N.V.

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Title	Discriminant face features extraction, analysis & its application in multipose face recognization: a survey
Authors	Shekapure, S.S. Kadam, N.V.
ORCID
Keywords	face recognition розпізнавання обличчя распознавание лица machine learning машинне навчання машинное обучение support vector machine classification класифікація классификация genetic algorithm генетичний алгоритм генетический алгоритм
Type	Conference Papers
Date of Issue	2017
URI	http://essuir.sumdu.edu.ua/handle/123456789/55763
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Citation	Shekapure, S.S. Discriminant face features extraction, analysis & its application in multipose face recognization: a survey [Текст] / S.S. Shekapure, N.V. Kadam // Advanced Information Systems and Technologies : proceedings of the V international scientific conference, Sumy, May 17-19 2017/ Edited by S.І. Protsenko, V.V. Shendryk. - Sumy : Sumy State University, 2017. - P. 98-100.
Abstract	As one of the excellent learning and classification performance, SVM and ISVM has become a research topic in the field of machine learning and has been applied in many areas, such as face detection and recognition, handwriting automatic identification and automatic text categorization. Face recognition is a challenging computer vision problem. Given a face database, goal of face recognition is to compare the input image class with all the classes and then declare a decision that identifies to whom the input image class belongs to or if it doesn’t belong to the database at all. In this survey, we study face recognition as a pattern classification problem.In this paper, we study the concept of SVM and sophisticated classification techniques for face recognition using the SVM and ISVM along with the advantages and disadvantages. This paper not only provides an up-to-date critical survey of machine learning techniques but also performance analysis of various SVM and ISVM techniques for face recognition are compared.
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