Thermal-Based Gender Recognition using Drones: Advancing Biometric Recognition in Challenging Outdoor Environments

Abstract

While biometric recognition typically uses features such as face, fingerprint, and iris to identify individuals, this study focuses on utilising specific characteristics to identify gender. The aim of this article is to propose a procedure for gender recognition under specific conditions. The specific condition addressed is outdoor area monitoring, which presents challenges such as varying lighting conditions and limited camera placement options. To tackle this, a proposed procedure utilises thermal images captured by the drone equipped with a thermal camera. The advantage of thermal images is their independence from ambient light conditions. The captured images are resized and processed using convolutional neural networks (CNN) (AlexNet, VGG‑16, VGG-19) for feature extraction and binary classification. A freely available database of thermal face images (FATFD) is used for training the CNNs, while a own created dataset of thermal images obtained by the drone (OCD) is used for testing. The findings indicate that the optimised CNNs achieve classification accuracies of 82.4% (VGG-16), 82.9% (AlexNet) and 85.5% (VGG-19). The original contribution of this study lies in demonstrating the suitability of face thermal images obtained through drones for gender recognition purposes.