We study the problem of facial analysis in videos. We propose a novel weakly supervised learning method that models the video event (expression, pain etc.) as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for smile, brow lower and cheek raise for pain). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations. In combination with complimentary features, we report state-of-the-art results on these datasets.

本文研究了视频中的面部分析问题，提出了一种新的弱监督学习方法，模拟视频事件（表情，疼痛等）作为自动挖掘的有区别子事件的序列，这个模型受到了多个实例学习和潜在SVM/HCRF框架的启发，用于模仿视频中的顺序或时间方面， 本文的贡献主要在于提出了这样的结构并在四个具有挑战性且公开可用的基于视频的面部分析数据集上实现了优异的表现。

LOMo：视频中面部分析的潜在顺序模型