计算机科学
提取器
水准点(测量)
特征提取
分类器(UML)
模式识别(心理学)
特征(语言学)
人工智能
面部表情
块(置换群论)
机器学习
工程类
地图学
数学
地理
语言学
哲学
几何学
工艺工程
作者
Jia Le Ngwe,Kian Ming Lim,Chin Poo Lee,Thian Song Ong
出处
期刊:Cornell University - arXiv
日期:2023-01-01
标识
DOI:10.48550/arxiv.2306.09626
摘要
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. The source code for the proposed method will be available at https://github.com/JLREx/PAtt-Lite.
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