Few Skeleton Features For Gate-Based Gender Recognition From Landmark Frames

Surveillance, forensic, and biometric systems are continually evolving with modern technology. Despite these advancements, their effectiveness in crime deterrence remains debatable, especially given the low-resolution nature of footage from distant cameras. Gait-based gender identification is a crucial application in surveillance. This paper investigates the use of minimal human skeleton points to derive discriminative features for gender recognition. We propose a new descriptor using the 7 Hu’s moments, applied to two regions: shoulders-hips and hips-knees, extracted from only 5 frames per sequence. Experiments were conducted using three databases (CASIA-B and two Kinect databases) and evaluated with SVM and KNN classifiers. Comparative analysis with established research highlights the effectiveness of our approach. Results confirm the reliability of shoulders-hips and hips-knees regions as biometric features for gender recognition across various factors. The highest accuracy achieved was 95.19% using the SVM classifier with the Kinect v2 dataset.