Robust Visitor Counting by Duplicate Face Detection

We present a novel duplicate face detection method inspired by a psychological memory model that explains the human cognitive process through sensory memory, short-term memory, and long-term memory to improve the accuracy of visitor counting. To achieve this, we propose a detection array (DA), a short-stay queue (SQ), and a long-stay queue (LQ), corresponding to the three memory types in the cognitive model. Two deep learning models, MobileNet and ResNet, are utilized to detect face areas and extract features, respectively. Detected faces in the current video frame are associated with the registered faces in the DA to identify counting targets. Subsequently, duplicate face removal was performed by sequentially matching the target faces with the registered faces in the SQ (managed by stay time) and the LQ (managed by queue length). Unmatched target faces are newly registered in the SQ, and the visitor count was then updated accordingly. In the experiments, the proposed method was evaluated using the QMUL and ChokePoint datasets. The experimental results demonstrate improved counting performance, with the proposed method achieving 100% accuracy for the QMUL dataset and 94.82% accuracy for the ChokePoint dataset.