There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Third, emotional data often suffers from missing modalities due to unforeseeable sensor malfunction or configuration issues. In this paper, we address all these problems under a novel multi-view deep generative framework. Specifically, we propose to model the statistical relationships of multi-modality emotional data using multiple modality-specific generative networks with a shared latent space. By imposing a Gaussian mixture assumption on the posterior approximation of the shared latent variables, our framework can learn the joint deep representation from multiple modalities and evaluate the importance of each modality simultaneously. To solve the labeled-data-scarcity problem, we extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. To address the missing-modality problem, we further extend our semi-supervised multi-view model to deal with incomplete data, where a missing view is treated as a latent variable and integrated out during inference. This way, the proposed overall framework can utilize all available (both labeled and unlabeled, as well as both complete and incomplete) data to improve its generalization ability. The experiments conducted on two real multi-modal emotion datasets demonstrated the superiority of our framework.
Multi-modal approaches have been widely implemented for emotion recognition @cite_36 @cite_25 @cite_9 @cite_16 @cite_19 @cite_23 @cite_20 . E.g., @cite_19 used a multi-modal deep belief network (DBN) to extract features from face, body gesture, voice and physiological signals for emotion classification. @cite_25 classified the combination of EEG and eye movement signals into three affective states. But, very few of them explored SSL. To the best of our knowledge, only @cite_34 proposed an enhanced multi-modal co-training algorithm for semi-supervised emotion recognition, but its shallow structure is hard to capture the high-level correlation between different modalities. In addition, most prior work in this field assumes that all modalities are available at all times @cite_34 @cite_20 , which is not realistic in practical environments. In contrast to the above methods, our framework naturally allows us to perform multi-modal emotion recognition within SSL and incomplete-data situations.
@cite_36 @cite_9 @cite_34 @cite_19 @cite_23 @cite_16 @cite_25 @cite_20
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In this paper we address the sentence-level multi-modal emotion recognition problem. We formulate the emotion recognition task as a multi-category classification problem and propose an innovative solution based on the automatically generated ensemble of trees with binary support vector machines (SVM) classifiers in the tree nodes. We demonstrate the efficacy of our approach by performing four-way (anger, happiness, sadness, neutral) and five-way (including excitement) emotion recognition on the University of Southern California's Interactive Emotional Motion Capture (USC-IEMOCAP) corpus using combinations of acoustic features, lexical features extracted from automatic speech recognition (ASR) output and visual features extracted from facial markers traced by a motion capture system. The experiments show that the proposed ensemble of trees of binary SVM classifiers outperforms classical multi-way SVM classification with one-vs-one voting scheme and achieves state-of-the-art results for all feature combinations. A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify the affective state of players during gameplay scenarios. More specifically, two monomodal classifiers have been designed for extracting affective state information based on facial expression and body motion analysis. For the combination of different modalities a deep model is proposed that is able to make a decision about player's affective state, while also being robust in the absence of one information cue. In order to evaluate the performance of our methodology, a bimodal database was created using Microsoft's Kinect sensor, containing feature vectors extracted from users' facial expressions and body gestures. The proposed method achieved higher recognition rate in comparison with mono-modal, as well as early-fusion algorithms. Our methodology outperforms all other classifiers, achieving an overall recognition rate of 98.3 . In this paper, we adopt a multimodal emotion recognition framework by combining eye movements and electroencephalography (EEG) to enhance emotion recognition. The main contributions of this paper are twofold. a) We investigate sixteen eye movements related to emotions and identify the intrinsic patterns of these eye movements for three emotional states: positive, neutral and negative. b) We examine various modality fusion strategies for integrating users external subconscious behaviors and internal cognitive states and reveal that the characteristics of eye movements and EEG are complementary to emotion recognition. Experiment results demonstrate that modality fusion could significantly improve emotion recognition accuracy in comparison with single modality. The best accuracy achieved by fuzzy integral fusion strategy is 87.59 , whereas the accuracies of solely using eye movements and EEG data are 77.80 and 78.51 , respectively Emotion analysis and recognition has become an interesting topic of research among the computer vision research community. In this paper, we first present the emoF-BVP database of multimodal (face, body gesture, voice and physiological signals) recordings of actors enacting various expressions of emotions. The database consists of audio and video sequences of actors displaying three different intensities of expressions of 23 different emotions along with facial feature tracking, skelet al tracking and the corresponding physiological data. Next, we describe four deep belief network (DBN) models and show that these models generate robust multimodal features for emotion classification in an unsupervised manner. Our experimental results show that the DBN models perform better than the state of the art methods for emotion recognition. Finally, we propose convolutional deep belief network (CDBN) models that learn salient multimodal features of expressions of emotions. Our CDBN models give better recognition accuracies when recognizing low intensity or subtle expressions of emotions when compared to state of the art methods.