Multimodal Emotion Recognition in HumanComputer Interaction Using MFF-CNN

Title

Multimodal Emotion Recognition in HumanComputer Interaction Using MFF-CNN

Creator

Ahmad A.; Singh V.; Upreti K.

Description

The rise of technology in the digital era has amplified the importance of understanding human emotions in enhancing humancomputer interactions. Traditional interfaces, mainly focused on logical tasks, often miss the nuances of human emotion, creating a gap between human users and technology. Addressing this gap, the development of the HumanComputer Interface for emotional intelligence uses advanced algorithms and deep learning models to accurately recognize emotions from various cues like facial expressions, voice, and written text. This paper presented a significant approach for emotion detection in HCI and the challenges faced in capturing genuine emotional responses. Historically, the emphasis in HCI design was on operational tasks, neglecting emotional nuances. However, the tide is changing toward embedding emotional intelligence into these interfaces, leading to enhanced user experiences. This research introduces the MFF-CNN, a neural network model combining both textual and visual data for accurate emotion detection. Through sophisticated algorithms and the integration of advanced machine learning techniques, this paper presents a refined approach to emotion detection in HCI, supported by a comprehensive review of related works and a detailed methodology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-1107 LNNS, pp. 49-59.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Deep learning; Emotion detection; HumanComputer Interaction (HCI); Machine learning; Multimodal data processing

Coverage

Ahmad A., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Singh V., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Upreti K., Department of Computer Science, Christ University, Ghaziabad, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981976580-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-6581-2_5

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214108183&doi=10.1007%2f978-981-97-6581-2_5&partnerID=40&md5=809deed1f2860f2a145db2f24bbd97b0