Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Nada Attar
Second Advisor
Saptarshi Sengupta
Third Advisor
Milind Bhusari
Keywords
Gender Classification, Convolution Neural Network [CNN], Computer Vision, PiFUHD, CLIP.
Abstract
In computer vision, gender classification has become a vital task having applications in human-computer interaction, healthcare, and surveillance. In this study, we look at a two-step approach based on human joint information for gender classification. In this research, we use convolutional neural networks (CNNs).
With Leeds Sports Pose (LSP) dataset, we use a C5 pre-trained model to map and extract joint information from 2D RGB images and after pre-processing and background removal, we use PiFUHD to transform these 2D images into 3D representations. Next, we train our models on RGB images and joint images for both 2D and 3D representations.
An empirical examination of our method produced encouraging results, with the accuracy reaching 80.8% solely on the joint images. Our results show the potential of using both 2D and 3D representations of images for gender classification tasks in addition to using cutting edge methods like CLIP and Tiramisu. This study offers insightful information for future developments in computer vision methods for applications that focus on people.
Recommended Citation
Dixit, Prathamesh, "GENDER CLASSIFICATION THROUGH POSTURAL ANALYSIS: A COMPARATIVE STUDY OF 2D IMAGES AND 3D RECONSTRUCTIONS." (2024). Master's Projects. 1409.
https://scholarworks.sjsu.edu/etd_projects/1409
