In this paper we address the problem of multi-cue affect recognition in challenging environments such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial expressions, as opposed to traditional methods that usually focus only on the latter. We evaluate our methods on a challenging child-robot interaction database of emotional expressions, as well as on a database of emotional expressions by actors, and show that the proposed method achieves significantly better results when compared with the facial expression baselines, can be trained both jointly and separately, and offers us computational models for both the individual modalities, as well as for the whole body emotion.
To be able to develop and test robust affective multimodal systems, researchers need access to novel databases containing representative samples of human multi-modal expressive behavior. The creation of such databases requires a major effort in the definition of representative behaviors, the choice of expressive modalities, and the collection and labeling of large amount of data. At present, public databases only exist for single expressive modalities such as facial expression analysis. There also exist a number of gesture databases of static and dynamic hand postures and dynamic hand gestures. However, there is not a readily available database combining affective face and body information in a genuine bimodal manner. Accordingly, in this paper, we present a bimodal database recorded by two high-resolution cameras simultaneously for use in automatic analysis of human nonverbal affective behavior Psychological research findings suggest that humans rely on the combined visual channels of face and body more than any other channel when they make judgments about human communicative behavior. However, most of the existing systems attempting to analyze the human nonverbal behavior are mono-modal and focus only on the face. Research that aims to integrate gestures as an expression mean has only recently emerged. Accordingly, this paper presents an approach to automatic visual recognition of expressive face and upper-body gestures from video sequences suitable for use in a vision-based affective multi-modal framework. Face and body movements are captured simultaneously using two separate cameras. For each video sequence single expressive frames both from face and body are selected manually for analysis and recognition of emotions. Firstly, individual classifiers are trained from individual modalities. Secondly, we fuse facial expression and affective body gesture information at the feature and at the decision level. In the experiments performed, the emotion classification using the two modalities achieved a better recognition accuracy outperforming classification using the individual facial or bodily modality alone. Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5 reported in the literature to 91.3 for a benchmark dataset on spontaneous emotion expressions. Psychologists have long explored mechanisms with which humans recognize other humans' affective states from modalities, such as voice and face display. This exploration has led to the identification of the main mechanisms, including the important role played in the recognition process by the modalities' dynamics. Constrained by the human physiology, the temporal evolution of a modality appears to be well approximated by a sequence of temporal segments called onset, apex, and offset. Stemming from these findings, computer scientists, over the past 15 years, have proposed various methodologies to automate the recognition process. We note, however, two main limitations to date. The first is that much of the past research has focused on affect recognition from single modalities. The second is that even the few multimodal systems have not paid sufficient attention to the modalities' dynamics: The automatic determination of their temporal segments, their synchronization to the purpose of modality fusion, and their role in affect recognition are yet to be adequately explored. To address this issue, this paper focuses on affective face and body display, proposes a method to automatically detect their temporal segments or phases, explores whether the detection of the temporal phases can effectively support recognition of affective states, and recognizes affective states based on phase synchronization alignment. The experimental results obtained show the following: 1) affective face and body displays are simultaneous but not strictly synchronous; 2) explicit detection of the temporal phases can improve the accuracy of affect recognition; 3) recognition from fused face and body modalities performs better than that from the face or the body modality alone; and 4) synchronized feature-level fusion achieves better performance than decision-level fusion. Abstract Affect presentation is periodic and multi-modal, such as through facial movements, body gestures, and so on. Studies have shown that temporal selection and multi-modal combinations may benefit affect recognition. In this article, we therefore propose a spatio-temporal fusion model that extracts spatio-temporal hierarchical features based on select expressive components. In addition, a multi-modal hierarchical fusion strategy is presented. Our model learns the spatio-temporal hierarchical features from videos by a proposed deep network, which combines a convolutional neural networks (CNN), bilateral long short-term memory recurrent neural networks (BLSTM-RNN) with principal component analysis (PCA). Our approach handles each video as a “video sentence.” It first obtains a skeleton with the temporal selection process and then segments key words with a certain sliding window. Finally, it obtains the features with a deep network comprised of a video-skeleton and video-words. Our model combines the feature level and decision level fusion for fusing the multi-modal information. Experimental results showed that our model improved the multi-modal affect recognition accuracy rate from 95.13 in existing literature to 99.57 on a face and body (FABO) database, our results have been increased by 4.44 , and it obtained a macro average accuracy (MAA) up to 99.71 .