Publication:
Object Recognition Using Convolutional Neural Network Architecture

dc.contributor.authorPrasantth a/l Subramaniamen_US
dc.date.accessioned2023-05-03T16:15:45Z
dc.date.available2023-05-03T16:15:45Z
dc.date.issued2020-02
dc.descriptionFYP 2 SEM 2 2019/2020en_US
dc.description.abstractEvolution of current modern era demands a fast and excellent way of computing day by day. It has dramatically transformed how we work and live. Involvement of Artificial Intelligence (AI) has significantly show the intelligent behavior side of today’s computers. Now machines are making intelligent decisions in recognizing objects, people, and languages. In has massively create an impact in a world driven by technology. My thesis illustrates the method of recognizing age and gender of a human in real time with the help of pre-trained deep learning models. In my project work, the age database has a total of 13,420 face images which has been separated into two different class; child and adult. The gender database has a total of 2,000 face images which has been separated into two different class; male and female. The main idea of this research is to study the performance of deep learning models by training the architectures consist of neural networks to classify images based on class and can be use in real time classification as well. The three best deep learning models which are VGG16, ResNet50 and MobileNet been selected based on their performance on training and validation accuracy. Hence, some classification on test images to witness the performance of the neural networks. My project work to be illustrated in this thesis.en_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21217
dc.language.isoenen_US
dc.subjecttensorflowen_US
dc.subjectdeep learningen_US
dc.subjectage gender classificationen_US
dc.titleObject Recognition Using Convolutional Neural Network Architectureen_US
dspace.entity.typePublication
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