| Title: |
A Deep Learning Framework with Optimizations for Facial Expression and Emotion Recognition from Videos |
| Authors: |
Nukathati, Ranjit Kumar; Nagella, Uday Bhaskar; Kumar, AP Siva |
| Source: |
International journal of electrical and computer engineering systems ; ISSN 1847-7003 (Online) ; ISSN 1847-6996 (Print) ; ISSN-L 1847-6996 ; Volume 16 ; Issue 3 |
| Publisher Information: |
Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek |
| Publication Year: |
2025 |
| Collection: |
Hrčak - Portal of scientific journals of Croatia / Portal znanstvenih časopisa Republike Hrvatske |
| Subject Terms: |
Emotion Recognition; Spatial Expression Analysis; Deep Learning; Artificial Intelligence; Hyperparameter Tuning |
| Description: |
Human emotion recognition has many real-time applications in healthcare and psychology domains. Due to the widespread usage of smartphones, large volumes of video content are being produced. A video can have both audio and video frames in the form of images. With the advancements in Artificial Intelligence (AI), there has been significant improvement in the development of computer vision applications.Accuracy in recognizing human emotions from given audio-visual content is a very challenging problem. However, with the improvements in deep learning techniques,analyzing audio-visual content towards emotion recognition is possible. The existing deep learning methods focused on audio content or video frames for emotion recognition. An integrated approach consisting of audio and video frames in a single framework is needed to leverage efficiency. This paper proposes a deep learning framework with specific optimizations for facial expression and emotion recognition from videos. We proposed an algorithm, Learning Human Emotion Recognition (LbHER), which exploits hybrid deep learning models that could process audio and video frames toward emotion recognition. Our empirical study with a benchmark dataset, IEMOCAP, has revealed that the proposed framework and the underlying algorithm could leverage state-of-the-art human emotion recognition. Our experimental results showed that the proposed algorithm outperformed many existing models with the highest average accuracy of 94.66%. Our framework can be integrated into existing computer vision applications to recognize emotions from videos automatically. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://doi.org/10.32985/ijeces.16.3.3; https://hrcak.srce.hr/329277 |
| DOI: |
10.32985/ijeces.16.3.3 |
| Availability: |
https://doi.org/10.32985/ijeces.16.3.3; https://hrcak.srce.hr/329277; https://hrcak.srce.hr/file/476167 |
| Rights: |
info:eu-repo/semantics/openAccess ; Copyright Authors of the International Journal of Electrical and Computer Engineering Systems must transfer copyright to the publisher in written form. Subscription Information The annual subscription rate is 50€ for individuals, 25€ for students and 150€ for libraries. |
| Accession Number: |
edsbas.D85E6388 |
| Database: |
BASE |