Spiking Feature-Driven Event Simulation with Movement-Aware Polarity Integration

Event-based face detection has attracted significant interest due to the unique advantages of event cameras, including high temporal resolution, high dynamic range, and low power consumption. However, the lack of annotated public datasets remains a major challenge for training effective event-based face detection models. In this paper, we propose a spiking feature-driven synthetic event generation framework that utilizes a spiking neural network (SNN) in conjunction with a pretrained convolutional backbone to generate synthetic event representations from a single RGB image. To incorporate motion-induced ON/OFF polarity information, we introduce a movement-aware polarity integration (MPI) module that assumes four directional facial movements. An event-similarity score is further employed to select representations most consistent with real event data for training. Unlike conventional approaches relying on video-based simulators, our method enables efficient synthetic event dataset construction without requiring video inputs or additional simulation training. Experimental results on the N-Caltech101 dataset demonstrate a face detection accuracy of 99.91%, outperforming existing event-based face detection methods.