PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition

Jia Le Ngwe, Kian Ming Lim, Chin Poo Lee, Thian Song Ong, Ali Alqahtani

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

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

Original languageEnglish
Pages (from-to)79327-79341
Number of pages15
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024
Externally publishedYes

Keywords

  • Facial expression recognition
  • MobileNetV1
  • patch extraction
  • self-attention

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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