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.
翻译:虽然已经付出了许多努力,但大多数努力的重点是在相对一致的主题和环境设置下,解决室内限制条件下的眼环检测问题。然而,在野外实际应用的眼环检测更为必要,而且存在更大的挑战。然而,据我们所知,我们以前没有很好地研究过这一点。在本文中,我们对这一研究主题做了说明。在野生数据集(即HUST-LEBW)中的673眼环链接视频样本(即381正数和292正数)中贴上了标签的眼环链接(即HUST-LEBW)中,这是由我们首先建立的。这些样本是从未受限制的电影中采集的眼环环链接检测问题,在人类的属性、人表情、不洁状况、成像配置等方面存在着巨大的差异。在利用SetetaFace引擎和KCFFC Trink视频样本中定位和跟踪人类眼睛的视频样本(即381正数和292正数)样本样本样本样本检测方法之后,还无法同时测量和测量现有软体图像。