Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11086
Title: Age and Gender Classification from Facial Features and Object Detection with Machine Learning
Authors: Karahan, M.
Lacinkaya, F.
Erdonmez, K.
Eminagaoglu, E.D.
Kasnakoglu, C.
Keywords: Age classification
Convolutional neural network
Face detection
Facial feature extraction
Gender classification
Machine learning
Object detection
Publisher: Research Expansion Alliance (REA)
Abstract: In recent years, development of the machine learning algorithms has led to the creation of intelligent surveillance systems. Thanks to the machine learning, it is possible to perform intelligent surveillance by recognizing people's facial features, classifying their age and gender, and detecting objects around instead of ordinary surveillance. In this study, a novel algorithm has been developed that classifies people's age and gender with a high accuracy rate. In addition, a novel object recognition algorithm has been developed that detects objects quickly and with high accuracy. In this study, age and gender classification was made based on the facial features of people using Convolutional Neural Network (CNN) architecture. Secondly, object detection was performed using different machine learning algorithms and the performance of the different machine learning algorithms was compared in terms of median average precision and inference time. The accuracy of the age and gender classification algorithm was tested using the Adience dataset and the results were graphed. The experimental results show that age and gender classification algorithms successfully classify people's age and gender. Then, the performances of object detection algorithms were tested using the COCO dataset and the results were presented in graphics. The experimental results stress that machine learning algorithms can successfully detect objects. © 2022, Research Expansion Alliance (REA). All rights reserved.
URI: https://doi.org/10.22105/jfea.2022.328472.1201
https://hdl.handle.net/20.500.11851/11086
ISSN: 2783-1442
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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