EmoVisioNet : a hybrid network unifying lightweight CNN and attention-based vision model for facial emotion detection

Mishra, Gargi (author), Bajpai, Supriya (author), Saini, Dharmender (author), Jain, Rachna (author), Jain, Deepak Kumar (author), Štruc, Vitomir (author)

Facial emotion detection has witnessed a surge in demand across numerous applications, including human-computer interaction, healthcare, and security. Accurate expression recognition is crucial for improving human-computer interactions and understanding human behavior. Existing facial emotion detection models face challenges in achieving both high accuracy and real-time processing due to complex architectures. Our goal is to create an efficient yet accurate solution that can work on resource-constrained devices. To address the challenge of accurately recognizing emotions from facial expressions, we propose a novel hybrid approach that combines the strengths of pretrained Lightweight Convolutional Neural Networks (CNNs), and Attention-based Vision Models. The pretrained Lightweight CNN serves as a feature extractor, efficiently capturing facial features, while the attention model refines the feature representation to focus on crucial regions of the face associated with different expressions. This enables our model to achieve state-of-the-art (SOTA) accuracy with reduced computational requirements. The proposed model, EmoVisioNet, achieves superior performance across multiple datasets, attaining 99.97 % accuracy on CK+, 96.23 % on RAF-DB, 93.88 % on FER2013, and 96.91 % on FERPlus. The obtained results surpass the current state-of-the-art in this field, demonstrating EmoVisioNet’s superior performance in facial expression recognition.

• Language: English
• Keywords: artificial intelligence, computer vision, machine learning, facial analysis
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 12 str.
• Numbering: Vol. 665, art.132224
• PID: 20.500.12556/RUL-176579
• UDC: 004.8
• ISSN on article: 1872-8286
• DOI: 10.1016/j.neucom.2025.132224
• COBISS.SI-ID: 259745027
• Publication date in RUL: 04.12.2025

Record is a part of a journal

• Title: Neurocomputing
• Publisher: Elsevier
• ISSN: 1872-8286
• COBISS.SI-ID: 152340995

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: umetna inteligenca, računalniški vid, strojno učenje, analiza obrazov