Non-Contact Infant Sleep Monitoring via Deep Learning-Based Facial Behavior Analysis

Sleep is essential for infant health and development, yet assessing infant sleep remains challenging because internal states must be inferred from observable behavior. This work presents a non-contact method for sleep-related behavioral inference based on facial analysis from video data. Unlike contact-based sensors or handcrafted feature approaches, the proposed system learns eye and mouth behavior directly from facial imagery and integrates these cues over time. Video frames are processed using facial region detection and landmark extraction, followed by MobileNet-based convolutional neural networks for binary classification of eye and mouth states. Frame-level predictions are aggregated through temporal decision logic to estimate sleep-related behavioral states, with blink dynamics and yawning serving as complementary indicators of transitional phases. Leveraging transfer learning enables efficient and robust inference suitable for real-world settings. Experimental results show reliable estimation of eye and mouth states and stable sleep-related behavioral inference. The proposed framework provides a practical, non-contact alternative to traditional infant sleep monitoring systems. Future work will address dataset expansion, robustness to occlusions and lighting variability, and the integration of multimodal inputs.