Multimodal human emotion recognition is an emerging area of research. It is a significant pattern recognition
problem that encourages researchers for the development of Multimodal human emotion detection systems. The Multimodal
databases contain text, audio, video or physiological signals of humans for the training, testing and validating the developed
Multimodal emotion recognition systems. This paper presents a systematic study of existing Multimodal human emotion
recognition databases. It describes various important stages involved in the construction of database namely design criteria,
recording environment, hardware setup, acquisition, and post-processing. It provides the summary of existing Multimodal
databases by common parameters such as elicitation method, number of participants, number of samples, duration of samples,
speaking language of participants, targeted modalities, annotation of samples and accessibility of the database. Further, it
provides valuable and possible future directions namely application specific, real-world situations and geographic regions
wise participants’ selection for the construction of Multimodal human emotion recognition databases.
Affective Computing, Emotion Recognition, Multimodal Databases, Multimodal Signal
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