The intrinsic interactions among a video's emotion tag, its content, and a user's spontaneous response while consuming the video can be leveraged to improve video emotion tagging, but this capability has not been thoroughly exploited yet. In this paper, we propose an implicit hybrid video emotion tagging approach by integrating video content and users' multiple physiological responses, which are only required during training. Specifically, multiple physiological signals during training construct a better emotion tagging model from video content. We add similarity constraints on the classifier mapping functions during training to capture the relationships among different kinds of features. We modify the traditional support vector machine with these constraints to improve video emotion tagging. Efficient learning algorithms of the proposed model are also developed. Experiments on three benchmark databases demonstrate the effectiveness and superior performance of our proposed method for implicitly integrating multiple physiological responses to improve video emotion tagging.

The main stream of current research of emotion recognition from electroencephalogram (EEG) signals infers users' emotion directly from EEG signals, without considering users' profile, which carries crucial information for emotion recognition. To address this, we propose a novel approach of emotion recognition from EEG signals enhanced by users' profile, which is only required during training. Specifically, we propose to use discriminative Restricted Boltzmann Machine (DRBM) to capture the inherent relationships among users' profile, EEG signals and emotion labels. During training, the proposed RBM classifier learns the mapping function from EEG signals and users' profile to emotion labels through discriminative learning algorithm. During testing, users' emotion can be recognized from EEG signals through marginalizing over users' profile, since RBM is a generative model. Experimental results on three benchmark databases, i.e. the DEAP database, the MAHNOB-HCI database and the USTC-ERVS database, demonstrate the effectiveness of the proposed approach in leveraging users' profile for enhancing emotion recognition from EEG signals and the superior emotion recognition performance of the proposed approach over existing approaches.

This paper proposes a novel deep two-view approach to learn features from both visible and thermal images and leverage the commonality among visible and thermal images for facial expression recognition from visible images. The thermal images are used as privileged information, which is required only during training to help visible images learn better features and classifier. Specifically, we first learn a deep model for visible images and thermal images respectively, and use the learned feature representations to train SVM classifiers for expression classification. We then jointly refine the deep models as well as the SVM classifiers for both thermal images and visible images by imposing the constraint that the outputs of the SVM classifiers from two views are similar. Therefore, the resulting representations and classifiers capture the inherent connections among visible facial image, infrared facial image and target expression labels, and hence improve the recognition performance for facial expression recognition from visible images during testing. Experimental results on the benchmark expression database demonstrate the effectiveness of our proposed method.

Current works on differentiating between posed and spontaneous facial expressions usually use features that are handcrafted for expression category recognition. Till now, no features have been specifically designed for differentiating between posed and spontaneous facial expressions. Recently, deep learning models have been proven to be efficient for many challenging computer vision tasks, and therefore in this paper we propose using the deep Boltzmann machine to learn representations of facial images and to differentiate between posed and spontaneous facial expressions. First, faces are located from images. Then, a two-layer deep Boltzmann machine is trained to distinguish posed and spon-tanous expressions. Experimental results on two benchmark datasets, i.e. the SPOS and USTC-NVIE datasets, demonstrate that the deep Boltzmann machine performs well on posed and spontaneous expression differentiation tasks. Comparison results on both datasets show that our method has an advantage over the other methods.