This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as the novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.
The Interactive Emotional Dyadic Motion Capture (IEMOCAP) @cite_5 dataset is one of the most used corpora for training emotion recognition systems. It contains 12 hours of audiovisual recordings of dialogue sessions between two actors. Each dialogue session is split into utterances and each utterance is annotated by five different annotators. Annotators evaluated the arousal valence and categorical emotion (Neutral, Happiness, Sadness, Anger, Surprise, Fear, Disgust, Frustration and Excitement) of each utterance. The annotators were instructed to watch and annotate the entire dialogue in sequence, in order to keep the contextual information in their annotations. This avoids improbable transitions in one dialogue (from frustrated to satisfied in a single sentence). Although this dataset is used by many researchers, it is heavily limited by the data collection method: The recordings happened in an indoor scenario, and although the subjects performed scripted and non-scripted dialogues, the instructions given to the subjects limited the types of emotions that can be expressed.
@cite_5
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Since emotions are expressed through a combination of verbal and non-verbal channels, a joint analysis of speech and gestures is required to understand expressive human communication. To facilitate such investigations, this paper describes a new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC). This database was recorded from ten actors in dyadic sessions with markers on the face, head, and hands, which provide detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios. The actors performed selected emotional scripts and also improvised hypothetical scenarios designed to elicit specific types of emotions (happiness, anger, sadness, frustration and neutral state). The corpus contains approximately 12 h of data. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication. This paper presents our contribution to ACM ICMI 2015 Emotion Recognition in the Wild Challenge (EmotiW 2015). We participate in both static facial expression (SFEW) and audio-visual emotion recognition challenges. In both challenges, we use a set of visual descriptors and their early and late fusion schemes. For AFEW, we also exploit a set of popularly used spatio-temporal modeling alternatives and carry out multi-modal fusion. For classification, we employ two least squares regression based learners that are shown to be fast and accurate on former EmotiW Challenge corpora. Specifically, we use Partial Least Squares Regression (PLS) and Kernel Extreme Learning Machines (ELM), which is closely related to Kernel Regularized Least Squares. We use a General Procrustes Analysis (GPA) based alignment for face registration. By employing different alignments, descriptor types, video modeling strategies and classifiers, we diversify learners to improve the final fusion performance. Test set accuracies reached in both challenges are relatively 25 above the respective baselines. In this paper we present the techniques used for the University of Montreal's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58 . Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03 on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56 . Automated emotion state tracking is a crucial element in the computational study of human communication behaviors. It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human-machine interfaces that facilitate efficient communication. We introduce a hierarchical computational structure to recognize emotions. The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications. The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation. We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database. In the case of the AIBO database, we obtain a balanced recall on each of the individual emotion classes using this hierarchical structure. The performance measure of the average unweighted recall on the evaluation data set improves by 3.37 absolute (8.82 relative) over a Support Vector Machine baseline model. In the USC IEMOCAP database, we obtain an absolute improvement of 7.44 (14.58 ) over a baseline Support Vector Machine modeling. The results demonstrate that the presented hierarchical approach is effective for classifying emotional utterances in multiple database contexts.