LOMo: Latent Ordinal Model for Facial Analysis in Videos
This work addresses facial analysis in videos for applications like expression and pain detection, but it is incremental as it extends existing frameworks to incorporate ordinal aspects.
The authors tackled facial analysis in videos by proposing a weakly supervised method that models events as sequences of automatically mined sub-events, achieving consistent improvements and state-of-the-art results on four challenging datasets for expression, pain, and intent prediction.
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.