Publication: Gender classification based on asian faces using deep learning
| dc.citedby | 5 | |
| dc.contributor.author | Janahiraman T.V. | en_US |
| dc.contributor.author | Subramaniam P. | en_US |
| dc.contributor.authorid | 57215350701 | en_US |
| dc.contributor.authorid | 57215359705 | en_US |
| dc.date.accessioned | 2023-05-29T07:23:08Z | |
| dc.date.available | 2023-05-29T07:23:08Z | |
| dc.date.issued | 2019 | |
| dc.description | 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 | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.nature | Final | en_US |
| dc.identifier.ArtNo | 8906399 | |
| dc.identifier.doi | 10.1109/ICSEngT.2019.8906399 | |
| dc.identifier.epage | 89 | |
| dc.identifier.scopus | 2-s2.0-85076421791 | |
| dc.identifier.spage | 84 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076421791&doi=10.1109%2fICSEngT.2019.8906399&partnerID=40&md5=5489aeb305ff85f61bcfd073b70f70a6 | |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/24382 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.source | Scopus | |
| dc.sourcetitle | 2019 IEEE 9th International Conference on System Engineering and Technology, ICSET 2019 - Proceeding | |
| dc.title | Gender classification based on asian faces using deep learning | en_US |
| dc.type | Conference Paper | en_US |
| dspace.entity.type | Publication |