PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition
Open accessAbstract
Facial Expression Recognition (FER) is a machine learning problem that deals with recognizing human facial expressions. While existing work has achieved performance improvements in recent years, FER in the wild and under challenging conditions remains a challenge. In this paper, a lightweight patch and attention network based on MobileNetV1, referred to as PAtt-Lite, is proposed to improve FER performance under challenging conditions. A truncated ImageNet-pre-trained MobileNetV1 is utilized as the backbone feature extractor of the proposed method. In place of the truncated layers is a patch extraction block that is proposed for extracting significant local facial features to enhance the representation from MobileNetV1, especially under challenging conditions. An attention classifier is also proposed to improve the learning of these patched feature maps from the extremely lightweight feature extractor. The experimental results on public benchmark databases proved the effectiveness of the proposed method. PAtt-Lite achieved state-of-the-art results on CK+, RAF-DB, FER2013, FERPlus, and the challenging conditions subsets for RAF-DB and FERPlus.
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Sherif, S., Ayoub, M., Aidaros, T., Yasser, Y., & Gamal, N. (2026) · IEEE International Conference on Smart Sustainable Systems for Computer and Engineering Applications (3SCEA) · doi:10.1109/3scea68071.2026.11603057
EmoPAtt-Lite: Lightweight Facial Emotion Recognition
Seddik, I. B., & Adelekan, A. E. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-21174-3_10
Lightweight Multi-task Emotion Recognition for Human-Robot Interaction
Yakushev, A., & Kashevnik, A. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-34387-1_1
Enhancing facial expression recognition with lightweight attention-based CNN
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Residual Spatial Gating with AU-Assisted Supervision for Facial Expression Recognition
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Lyu, Y., Zou, Y., Hu, J., Li, Y., & Li, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37189-8_1
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Baskaran, S., Jegan, R. K., Murali, S., & Palaniswamy, S. (2026) · International Conference on Natural Language Processing and Computer Vision (ICNPCV) · doi:10.1109/icnpcv69434.2026.11708800
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- Authors
- Ngwe, J. L., Lim, K. M., Lee, C. P., Ong, T. S., & Alqahtani, A.
- Affiliations
- Multimedia University (MY)
- King Khalid University (SA)
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 79327-79341
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4399168297
- Cited by
- 45
- References
- 52
- Citations by year
- 2026: 14 · 2025: 21 · 2024: 10
- FWCI
- 18.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Telekom Malaysia Research and Development (RDTC/231084)
- Deanship of Scientific Research, King Khalid University, Saudi Arabia (RGP2/332/44)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression recognition, lightweight neural networks, MobileNetV1, patch extraction, attention mechanism, local facial features, feature extraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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