Self-supercised learning in gender classification using full-body images extracted from CCTV footage

Abstract

Gender classification is regarded as one of the vital components of security systems, recommendation systems, data access authentication and surveillance. Facial features and supervised learning remain the predominant metrics to classify genders currently. But facial feature driven approach would falter in case of incomplete or unavailable details especially when analyzing masked faces or CCTV footage and supervised learning driven approach becomes tedious and time-consuming provided large volume of labelled data. Therefore, the need of analyzing full-body images is established instead of the sole focus of facial features driven analysis as well as the less dependency on supervised learning. The proposed approach establishes the implementation of convolutional neural network (CNN) based on self-supervised learning classification algorithm that needs fewer volumes of labelled data for fine-tuning. dBOT classifier, a state-of-the-art self-supervised image classification model, is used to perform transfer learning and the subsequential fine-tuning to facilitate the training on low-quality images. The proposed model on evaluation significantly outperforms SSL based methods for small, unclear full-body gender image classification techniques applied on CCTV footage extracts. Keywords: CNN, dBOT, Gender-Classification, CCTV