Gender classification based on asian faces using deep learning
Face recognition; Human computer interaction; Image classification; Learning algorithms; Natural language processing systems; Neural networks; Speech recognition; Systems engineering; Convolutional neural network; Gender classification; Learning architectures; Learning frameworks; NAtural language p...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-243822023-05-29T15:23:08Z Gender classification based on asian faces using deep learning Janahiraman T.V. Subramaniam P. 57215350701 57215359705 Face recognition; Human computer interaction; Image classification; Learning algorithms; Natural language processing systems; Neural networks; Speech recognition; Systems engineering; Convolutional neural network; Gender classification; Learning architectures; Learning frameworks; NAtural language processing; Recognition accuracy; Recognition process; TensorFlow; Deep learning For the past few years, gender classification has been an active area of study and researchers have been putting a lot of effort to contribute quality research in this area. There is a big potential field of study as it can be used in monitoring, surveillance and human-computer interaction. However, there is still a lack of the performance of existing methods on real live images. The rise of deep learning algorithm has been showing a spectacular increase in performance lately. Many difficult tasks involving computer vision, speech recognition, and natural language processing are easily solved with deep learning. Therefore, the approach to deep learning notably growing and this also happens to be on image classification. Gender classification is an important subject in the face recognition process. This paper shows the results of classifying gender using Convolutional Neural Network based Deep Learning architectures using Tensorflow's Deep Learning framework. We have used models provided by Keras with weights pre-trained on ImageNet. We have made a comparison of the different type of models which includes VGG16, ResNet-50, and MobileNet. Our own database consists of Asian faces inclusive of Malaysians and some Caucasians. Our trained model on a database consisting of 1000 images shows that VGG-16 delivered the highest recognition accuracy. � 2019 IEEE. Final 2023-05-29T07:23:08Z 2023-05-29T07:23:08Z 2019 Conference Paper 10.1109/ICSEngT.2019.8906399 2-s2.0-85076421791 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076421791&doi=10.1109%2fICSEngT.2019.8906399&partnerID=40&md5=5489aeb305ff85f61bcfd073b70f70a6 https://irepository.uniten.edu.my/handle/123456789/24382 8906399 84 89 Institute of Electrical and Electronics Engineers Inc. Scopus |
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Face recognition; Human computer interaction; Image classification; Learning algorithms; Natural language processing systems; Neural networks; Speech recognition; Systems engineering; Convolutional neural network; Gender classification; Learning architectures; Learning frameworks; NAtural language processing; Recognition accuracy; Recognition process; TensorFlow; Deep learning |
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57215350701 |
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57215350701 Janahiraman T.V. Subramaniam P. |
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Conference Paper |
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Janahiraman T.V. Subramaniam P. |
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Janahiraman T.V. Subramaniam P. Gender classification based on asian faces using deep learning |
author_sort |
Janahiraman T.V. |
title |
Gender classification based on asian faces using deep learning |
title_short |
Gender classification based on asian faces using deep learning |
title_full |
Gender classification based on asian faces using deep learning |
title_fullStr |
Gender classification based on asian faces using deep learning |
title_full_unstemmed |
Gender classification based on asian faces using deep learning |
title_sort |
gender classification based on asian faces using deep learning |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2023 |
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1806424303241003008 |
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13.214268 |