A Lightweight Passive Depression Detection System Based on Interpretable Facial Feature Analysis

Automatic depression detection from facial videos is promising for IoT deployment but has long suffered from the black-box nature of deep models, which overlook how depression manifests on the face. This lack of interpretability leads to redundant feature learning, overly complex architectures, and consequently a trade-off between accuracy and deployability. We introduce a lightweight, interpretable system that explicitly models static–dynamic facial patterns, color channels, and regional textures. At its core is a Hybrid Static–Dynamic Model (HSDM) with detachable modules and identity decoupling, supported by two task-driven preprocessing steps (blue-channel suppression and 90° Gabor filtering). On AVEC2013/2014, our approach reduces mean absolute error by 10.1% on AVEC2014 while maintaining competitive results on AVEC2013. From a systems perspective, it achieves real-time inference on Jetson Nano (0.071M parameters, 0.21 GFLOPs, 17.69 ms latency, 56.54 samples/s throughput), outperforming larger SOTA models under identical conditions. The design facilitates on-device deployment and offers transparent decision paths through interpretable static–dynamic fusion, color synergy, and region-level analysis.