Gender Classification Accuracy via Two-Dimensional Body Joints using Convolutional Neural Networks

With the increasing demand for gender-related data in various applications such as border security systems and targeted marketing, gender of female and male classification has gained significant importance in the field of computer vision and deep learning. Existing research has focused on gender classification through facial image, external appearance, or gait movement. However, there is a lack of studies specifically targeting gender classification using two-dimensional body joints. This paper introduces a novel prediction pipeline to enhance the accuracy of gender classification based solely on two-dimensional joint images. Our proposed approach utilizes deep learning (Convolutional Neural Network) technique. We conducted experiments on the BBC Pose and Short BBC Pose datasets, preprocessing the images by filtering out frames with missing human figures, removing background noise, and labeling the joints through transfer learning with a C5 pre-trained model based on ResNet-152. Our results demonstrate that the deep learning method outperforms other approaches, successfully classifying gender (female and male) using two-dimensional joint images and achieving an accuracy of 66.5%.