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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 onaddressing the eyeblink detection problem under the constrained indoorconditions with the relative consistent subject and environment setup.Nevertheless, towards the practical applications eyeblink detection in the wildis more required, and of greater challenges. However, to our knowledge this hasnot been well studied before. In this paper, we shed the light to this researchtopic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673eyeblink video samples (i.e., 381 positives, and 292 negatives) is firstestablished by us. These samples are captured from the unconstrained movies,with the dramatic variation on human attribute, human pose, illuminationcondition, imaging configuration, etc. Then, we formulate eyeblink detectiontask as a spatial-temporal pattern recognition problem. After locating andtracking human eye using SeetaFace engine and KCF tracker respectively, amodified LSTM model able to capture the multi-scale temporal information isproposed to execute eyeblink verification. A feature extraction approach thatreveals appearance and motion characteristics simultaneously is also proposed.The experiments on HUST-LEBW reveal the superiority and efficiency of ourapproach. It also verifies that, the existing eyeblink detection methods cannotachieve satisfactory performance in the wild.

Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices