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Title: Face Detection and Facial Feature Extraction with Machine Learning
Authors: Karahan, Mehmet
Laçinkaya, F.
Erdönmez, K.
Eminağaoğlu, E.D.
Kasnakoğlu, Coşku
Keywords: AdaBoost
Age classification
Convolutional neural network
Face detection
Facial feature extraction
Gender classification
Viola-Jones face detector
Issue Date: 2022
Publisher: Springer Science and Business Media Deutschland GmbH
Abstract: Face detection is important part of surveillance systems and it has been widely used in computer vision and image processing. Face detection is also first step of the facial feature extraction. Facial feature extraction is a topic that has been focused on by many researchers in computer science, psychology, medicine and related fields and has become increasingly important in recent years. With the help of facial features, machine learning algorithms can estimate ages and classify genders of people. In this paper, face detection, facial feature extraction, age estimation and gender classification are presented. Firstly, face detection and extraction of facial features like eyes, eyebrows, mouth and nose are presented. Secondly, age estimation and gender classification based on the extracted facial features are explained. Experimental results prove that face detection algorithm efficiently detects human faces and facial feature algorithm accurately locates eyes, eyebrows, mouth and nose. Experimental results also show that, based on the extracted facial features, convolutional neural network architecture estimates ages of the people and classifies their gender. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Description: International Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021 -- 264409
ISBN: 9783030855765
ISSN: 2367-3370
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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