Explainable 3D Convolutional Neural Networks Spatiotemporal Learning for Human Handshake Interaction Recognition

Human Activity Recognition (HAR) has gained significant attention in computer vision due to its wide range of applications in surveillance, social behaviour analysis, and human–computer interaction. Among various human-to-human interactions, handshake recognition is particularly important as it represents social intention and cooperative behaviour. This study presents an efficient and interpretable deep learning framework for automatic handshake recognition from video sequences. The proposed approach employs a pretrained 3D Convolutional Neural Network (3D CNN) to directly learn spatiotemporal features, enabling effective modelling of both motion dynamics and spatial relationships between interacting individuals. The experiments are conducted using two dataset namely UT-Interaction Human Interaction Dataset and SBU Kinect Interaction dataset, focusing exclusively on the handshake interaction as the target class. The dataset provides accurate ground-truth annotations, including temporal intervals and bounding boxes, which support precise localization and reliable recognition of handshake actions. Each dataset is split into 80% for training, 10% for validation, and 10% for testing to ensure robust performance evaluation. The experimental results demonstrated that the proposed 3D CNN-based framework achieved a handshake recognition accuracy of 98.92% on the UT-Interaction dataset, representing performance improvements of 9.72%, 6.32%, and 7.12% compared to CNN, BiLSTM, and RNN models, respectively.