Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from uncon-strainedfilms to reveal “in the wild“ characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counter-parts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.

Real-time eyeblink detection in the wild is a recently emerged challenging task that suffers from dramatic variations in face attribute, pose, illumination, camera view and distance, etc. One key issue is to well characterize eyelid’s intrinsic motion (i.e., approaching and departure between upper and lower eyelid) robustly, under unconstrained conditions. Towards this, a novel eyelid’s intrinsic motion-aware feature learning approach is proposed. Our proposition lies in 3 folds. First, the feature extractor is led to focus on informative eye region adaptively via introducing visual attention in a coarse-to-fine way, to guarantee robustness and fine-grained descriptive ability jointly. Then, 2 constraints are proposed to make feature learning be aware of eyelid’s intrinsic motion. Particularly, one concerns the fact that the inter-frame feature divergence within eyeblink processes should be greater than non-eyeblink ones to better reveal eyelid’s intrinsic motion. The other constraint minimizes the inter-frame feature divergence of non-eyeblink samples, to suppress motion clues due to head or camera movement, illumination change, etc. Meanwhile, concerning the high ambiguity between eyeblink and non-eyeblink samples, soft sample labels are acquired via self-knowledge distillation to conduct feature learning with finer supervision than the hard ones. The experiments verify that, our proposition is significantly superior to the state-of-the-art ones (i.e., advantage on F1-score over 7%) and with real-time running efficiency. It is also of strong generalization capacity towards constrained conditions. The source code is available athttps://github.com/wenzhengzeng/blink_eyelid.