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.
Nguyen al @cite_12 proposed a latent SVM based algorithm for classifying and localizing events in a time-series. They later proposed a fully supervised structured SVM for predicting Action Unit segments in video sequences @cite_20 . Our algorithm differs from @cite_12 , while they use simple MIL, we detect multiple prototypical segments and further learn their temporal ordering. MIL based algorithm has also been used for predicting pain @cite_25 . In recent works, MIL has been used with HMM @cite_29 and also to learn embedding for multiple concepts @cite_6 for predicting facial expressions. Rudovic al @cite_17 proposed a CRF based model that accounted for ordinal relationships between expression intensities. Our work differs from this work in handling weakly labeled data and modeling the ordinal sequence between sub-events (see ).
@cite_29 @cite_6 @cite_12 @cite_25 @cite_20 @cite_17
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We present a compositional model for video event detection. A video is modeled using a collection of both global and segment-level features and kernel functions are employed for similarity comparisons. The locations of salient, discriminative video segments are treated as a latent variable, allowing the model to explicitly ignore portions of the video that are unimportant for classification. A novel, multiple kernel learning (MKL) latent support vector machine (SVM) is defined, that is used to combine and re-weight multiple feature types in a principled fashion while simultaneously operating within the latent variable framework. The compositional nature of the proposed model allows it to respond directly to the challenges of temporal clutter and intra-class variation, which are prevalent in unconstrained internet videos. Experimental results on the TRECVID Multimedia Event Detection 2011 (MED11) dataset demonstrate the efficacy of the method. We propose a weakly-supervised structured learning approach for recognition and spatio-temporal localization of actions in video. As part of the proposed approach, we develop a generalization of the Max-Path search algorithm which allows us to efficiently search over a structured space of multiple spatio-temporal paths while also incorporating context information into the model. Instead of using spatial annotations in the form of bounding boxes to guide the latent model during training, we utilize human gaze data in the form of a weak supervisory signal. This is achieved by incorporating eye gaze, along with the classification, into the structured loss within the latent SVM learning framework. Experiments on a challenging benchmark dataset, UCF-Sports, show that our model is more accurate, in terms of classification, and achieves state-of-the-art results in localization. In addition, our model can produce top-down saliency maps conditioned on the classification label and localized latent paths. This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose. We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets.