Exploiting facial emotion recognition system for ambient assisted living technologies triggered by interpreting the user's emotional state
Our attention scheme is architecture-agnostic, adds minimal computational overhead, and markedly boosts FER accuracy on low-resolution faces. Coupling the probability distribution directly to the RGB gamut provides a fine-grained, perceptually meaningful channel for affect-adaptive AAL systems.
Authors
- Samuele Russo (ORCID: https://orcid.org/0000-0002-1846-9996)
- Cristian Randieri (ORCID: https://orcid.org/0000-0001-5300-3561)
- Christian Napoli (ORCID: https://orcid.org/0000-0002-3336-5853)
- Abdelaziz Rabehi (ORCID: https://orcid.org/0000-0001-8684-4754)
- Imad Eddine Tibermacine (ORCID: https://orcid.org/0009-0004-4729-7128)
- El‐Sayed M. El‐kenawy (ORCID: https://orcid.org/0000-0002-9221-7658)
- Amal H. Alharbi (ORCID: https://orcid.org/0000-0001-9327-7284)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Częstochowa University of Technology (PL)
- Applied Science Private University (JO)
- Università degli Studi eCampus (IT)
- Ziane Achour University of Djelfa (DZ)
- National Research Council (IT)
- Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti (IT)
- Bahrain Polytechnic (BH)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-08-28
- DOI
- https://doi.org/10.3389/fnins.2025.1622194
- Citations
- 6
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 12.71