Effective and real-time eyeblink detection is of wide-range applications,
such as deception detection, drive fatigue detection, face anti-spoofing, etc.
Although numerous of efforts have already been paid, most of them focus on
addressing the eyeblink detection problem under the constrained indoor
conditions with the relative consistent subject and environment setup.
Nevertheless, towards the practical applications eyeblink detection in the wild
is more required, and of greater challenges. However, to our knowledge this has
not been well studied before. In this paper, we shed the light to this research
topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673
eyeblink video samples (i.e., 381 positives, and 292 negatives) is first
established by us. These samples are captured from the unconstrained movies,
with the dramatic variation on human attribute, human pose, illumination
condition, imaging configuration, etc. Then, we formulate eyeblink detection
task as a spatial-temporal pattern recognition problem. After locating and
tracking human eye using SeetaFace engine and KCF tracker respectively, a
modified LSTM model able to capture the multi-scale temporal information is
proposed to execute eyeblink verification. A feature extraction approach that
reveals appearance and motion characteristics simultaneously is also proposed.
The experiments on HUST-LEBW reveal the superiority and efficiency of our
approach. It also verifies that, the existing eyeblink detection methods cannot
achieve satisfactory performance in the wild.

本文研究实时眨眼检测技术在野外环境中的应用。作者提出了一个人眼位置和轨迹跟踪后使用改进的 LSTM 模型识别多尺度时序信息的眨眼检测方法，并建立了适用于野外环境的眨眼数据集（HUST-LEBW）以验证该方法的可行性和效率。