Survey on audiovisual emotion recognition: databases, features, and data fusion strategies
Open Access
- 11 November 2014
- journal article
- research article
- Published by Now Publishers in APSIPA Transactions on Signal and Information Processing
- Vol. 3 (1)
- https://doi.org/10.1017/atsip.2014.11
Abstract
Emotion recognition is the ability to identify what people would think someone is feeling from moment to moment and understand the connection between his/her feelings and expressions. In today's world, human–computer interaction (HCI) interface undoubtedly plays an important role in our daily life. Toward harmonious HCI interface, automated analysis and recognition of human emotion has attracted increasing attention from the researchers in multidisciplinary research fields. In this paper, a survey on the theoretical and practical work offering new and broad views of the latest research in emotion recognition from bimodal information including facial and vocal expressions is provided. First, the currently available audiovisual emotion databases are described. Facial and vocal features and audiovisual bimodal data fusion methods for emotion recognition are then surveyed and discussed. Specifically, this survey also covers the recent emotion challenges in several conferences. Conclusions outline and address some of the existing emotion recognition issues.Keywords
This publication has 90 references indexed in Scilit:
- LSTM-Modeling of continuous emotions in an audiovisual affect recognition frameworkImage and Vision Computing, 2013
- The MAHNOB Laughter databaseImage and Vision Computing, 2013
- Categorical and dimensional affect analysis in continuous input: Current trends and future directionsImage and Vision Computing, 2013
- Survey on speech emotion recognition: Features, classification schemes, and databasesPattern Recognition, 2011
- First Impressions of the Face: Predicting SuccessSocial and Personality Psychology Compass, 2010
- Being bored? Recognising natural interest by extensive audiovisual integration for real-life applicationImage and Vision Computing, 2009
- Facial expression recognition based on Local Binary Patterns: A comprehensive studyImage and Vision Computing, 2009
- IEMOCAP: interactive emotional dyadic motion capture databaseLanguage Resources and Evaluation, 2008
- A robust multimodal approach for emotion recognitionNeurocomputing, 2008
- A coupled HMM approach to video-realistic speech animationPattern Recognition, 2007