Special Issue "Sensor Based Multi-Modal Emotion Recognition"
Deadline for manuscript submissions: 30 November 2022 | Viewed by 10975
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.
Interests: deep-learning-based emotion recognition; medical image analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Emotion recognition is one of the hot issues in AI research. This Special Issue is being assembled to share all kinds of in-depth research results related to emotion recognition, such as the classification of emotion category (anger, disgust, fear, happiness, sadness, surprise, neutral, etc.), arousal/valence estimation, diagnosis of mental health such as stress, pain, cognitive load, engagement, curiosity, humor, and so on. All of these problems deal with a stream of data not only from individual sensors such as RGB-D cameras, EEG/ECG/EMG sensors, wearable devices, or smart phones, but also from the fusion of various sensors.
Please join this Special Issue entitled “Sensor-Based Multi-Modal Emotion Recognition”, and contribute your valuable research progress. Thank you very much.
Prof. Soo-Hyung Kim
Prof. Gueesang Lee
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- multi-modal emotion recognition
- audio-visual, EEG/ECG/EMG, wearable devices
- emotion classification
- arousal/valence estimation
- stress, pain, cognitive load, engagement, curiosity, humor
- related issues in emotion recognition or sentiment analysis
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Subjective Evaluation of Basic Emotions from Audio-Visual Data
Authors: Sudarsana Reddy Kadiri; Paavo Alku
Affiliation: Department of Signal Processing and Acoustics, Aalto University, Finland
Abstract: Understanding of the perception of emotions or affective states in humans is important to develop emotion-aware systems that work in realistic scenarios. In this paper, the perception of emotions in naturalistic human interaction (audio-visual data) is studied using perceptual evaluation. For this purpose, a naturalistic audio-visual emotion database collected from TV broadcasts like soap-operas and movies, called the IIIT-H Audio-Visual Emotion (IIIT-H AVE) database, is used. The database consists of audio-alone, video-alone, and audio-visual data in English. Using data of all these three modes, perceptual tests are conducted for four basic emotions (angry, happy, neutral and sad) based on category labeling and for two dimensions namely arousal (active or passive) and valence (positive or negative) based on dimensional labeling. Interestingly, the general patterns in the perception of emotions were remarkably different for different emotions. This finding emphasizes the importance of emotion-specific features compared to commonly used features in the development of emotion-aware systems.