Paper 2025/1280

SecFePAS: Secure Facial-Expression-Based Pain Assessment with Deep Learning at the Edge

Abstract

Patient monitoring in hospitals, nursing centers, and home care can be largely automated using cameras and machine-learning-based video analytics, thus considerably increasing the efficiency of patient care. In particular, Facial-expression-based Pain Assessment Systems (FePAS) can automatically detect pain and notify medical personnel. However, current FePAS solutions using cloud-based video analytics offer very limited security and privacy protection. This is problematic, as video feeds of patients constitute highly sensitive information. To address this problem, we introduce SecFePAS, the first FePAS solution with strong security and privacy guarantees. SecFePAS uses advanced cryptographic protocols to perform neural network inference in a privacy-preserving way. To counteract the significant overhead of the used cryptographic protocols, SecFePAS uses multiple optimizations. First, instead of a cloud-based setup, we use edge computing with a 5G connection to benefit from lower network latency. Second, we use a combination of transfer learning and quantization to devise neural networks with high accuracy and optimized inference time. Third, SecFePAS quickly filters out unessential frames of the video to focus the in-depth analysis on key frames. We tested SecFePAS with the SqueezeNet and ResNet50 neural networks on a real pain estimation benchmark. SecFePAS outperforms state-of-the-art FePAS systems in accuracy and optimizes secure processing time.