Citation

Ngwe, Jia Le and Lim, Kian Ming and Lee, Chin Poo and Ong, Thian Song and Alqahtani, Ali (2024) PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition. IEEE Access, 12. pp. 79327-79341. ISSN 2169-3536

Text PAtt-Lite_ Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition.pdf - Published Version Restricted to Repository staff only Download (5MB)

Abstract

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. PAttLite achieved state-of-the-art results on CK+, RAF-DB, FER2013, FERPlus, and the challenging conditions subsets for RAF-DB and FERPlus.

Item Type:	Article
Uncontrolled Keywords:	Facial expression
Subjects:	R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis Z Bibliography. Library Science. Information Resources > ZA3038-5190 Information resources (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jul 2024 02:32
Last Modified:	03 Jul 2024 02:32
URII:	http://shdl.mmu.edu.my/id/eprint/12575

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