{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:07:36Z","timestamp":1787029656465,"version":"build-2736575974"},"reference-count":50,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:00:00Z","timestamp":1750291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2024","award":["20022362"],"award-info":[{"award-number":["20022362"]}]},{"name":"Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2024","award":["2410003714"],"award-info":[{"award-number":["2410003714"]}]},{"name":"Establishment of standardization basis for BCI and AI Interoperability","award":["20022362"],"award-info":[{"award-number":["20022362"]}]},{"name":"Establishment of standardization basis for BCI and AI Interoperability","award":["2410003714"],"award-info":[{"award-number":["2410003714"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Facial emotion recognition (FER) is vital for improving human\u2013machine interactions, serving as the foundation for AI systems that integrate cognitive and emotional intelligence. This helps bridge the gap between mechanical processes and human emotions, enhancing machine engagement with humans. Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) feature extraction techniques. By integrating these features into a specially modified AlexNet architecture, our approach not only classifies facial emotions more accurately but also demonstrates significant improvements in performance and adaptability under various operational conditions. To validate the effectiveness of our proposed model, we conducted evaluations using the FER2013 and RAF-DB benchmark datasets, where it achieved impressive accuracies of 98.10% and 93.34% for the two datasets, with standard deviations of 1.63% and 3.62%, respectively. On the FER-2013 dataset, the model attained a precision of 98.2%, a recall of 97.9%, and an F1-score of 98.0%. Meanwhile, for the other dataset, it achieved a precision of 93.54%, a recall of 93.12%, and an F1-score of 93.34%. These results underscore the model\u2019s robustness and its capability to deliver high-precision emotion recognition, making it an ideal solution for deployment in environments where hardware limitations are a critical concern.<\/jats:p>","DOI":"10.3390\/s25123832","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T03:05:27Z","timestamp":1750388727000},"page":"3832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced AlexNet with Gabor and Local Binary Pattern Features for Improved Facial Emotion Recognition"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6612-8176","authenticated-orcid":false,"given":"Furkat","family":"Safarov","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alpamis","family":"Kutlimuratov","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Kimyo International University in Tashkent, Tashkent 100121, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ugiloy","family":"Khojamuratova","sequence":"additional","affiliation":[{"name":"Department of Computer Science, CUNY Queens College, 65-30 Kissena Blvd Flushing, New York, NY 11374, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5923-8695","authenticated-orcid":false,"given":"Akmalbek","family":"Abdusalomov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-7599","authenticated-orcid":false,"given":"Young-Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TAFFC.2020.2981446","article-title":"Deep Facial Expression Recognition: A Survey","volume":"13","author":"Li","year":"2022","journal-title":"IEEE Trans. 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