用于野外复合面部表情识别的标签分布学习:比较研究

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Expert Systems Pub Date : 2024-09-10 DOI:10.1111/exsy.13724
Afifa Khelifa, Haythem Ghazouani, Walid Barhoumi
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引用次数: 0

摘要

人类的情绪状态包括基本面部表情和复合面部表情。然而,目前的研究主要集中在基本表情上,因此忽略了实际场景中遇到的广泛的人类情绪。复合面部表情涉及个人面部多种情绪的同时表现。这种现象反映了人类状态的复杂性和丰富性,面部特征动态地传达了多种情感的组合。本研究对复合面部表情识别(CFER)进行了开创性的探索,并特别强调利用标签分布学习(LDL)范式。LDL 的这一战略性应用旨在解决复合表情固有的模糊性和复杂性,标志着与主流的单标签学习(SLL)和多标签学习(MLL)范式的重大差异。在这一框架内,我们严格研究了 LDL 在面部表情识别(FER)的关键挑战中的潜力:在不受控制的环境中识别复合面部表情。我们利用最近推出的 RAF-CE 数据集,该数据集是专为复合表情评估而精心设计的。通过在 RAF-CE 数据集上对 LDL 与传统 SLL 和 MLL 方法进行全面的比较分析,我们旨在明确 LDL 在处理这一复杂任务方面的优势。此外,我们还通过评估在 EmotioNet 和 RAF-DB Compound 数据集上的表现,评估了在 RAF-CE 上训练的 LDL 模型的通用性。这证明了它们在没有领域适应的情况下的有效性。为了巩固这些研究结果,我们在 RAF-CE、S-BU3DFE 和 S-JAFFE 数据集上对 12 种前沿 LDL 算法进行了全面的比较分析,从而为 FER 在实际应用中最有效的 LDL 技术提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Label distribution learning for compound facial expression recognition in‐the‐wild: A comparative study
Human emotional states encompass both basic and compound facial expressions. However, current works primarily focus on basic expressions, consequently neglecting the broad spectrum of human emotions encountered in practical scenarios. Compound facial expressions involve the simultaneous manifestation of multiple emotions on an individual's face. This phenomenon reflects the complexity and richness of human states, where facial features dynamically convey a combination of feelings. This study embarks on a pioneering exploration of Compound Facial Expression Recognition (CFER), with a distinctive emphasis on leveraging the Label Distribution Learning (LDL) paradigm. This strategic application of LDL aims to address the ambiguity and complexity inherent in compound expressions, marking a significant departure from the dominant Single Label Learning (SLL) and Multi‐Label Learning (MLL) paradigms. Within this framework, we rigorously investigate the potential of LDL for a critical challenge in Facial Expression Recognition (FER): recognizing compound facial expressions in uncontrolled environments. We utilize the recently introduced RAF‐CE dataset, meticulously designed for compound expression assessment. By conducting a comprehensive comparative analysis pitting LDL against conventional SLL and MLL approaches on RAF‐CE, we aim to definitively establish LDL's superiority in handling this complex task. Furthermore, we assess the generalizability of LDL models trained on RAF‐CE by evaluating their performance on the EmotioNet and RAF‐DB Compound datasets. This demonstrates their effectiveness without domain adaptation. To solidify these findings, we conduct a comprehensive comparative analysis of 12 cutting‐edge LDL algorithms on RAF‐CE, S‐BU3DFE, and S‐JAFFE datasets, providing valuable insights into the most effective LDL techniques for FER in‐the‐wild.
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来源期刊
Expert Systems
Expert Systems 工程技术-计算机:理论方法
CiteScore
7.40
自引率
6.10%
发文量
266
审稿时长
24 months
期刊介绍: Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper. As well as traditional application areas, such as Software and Requirements Engineering, Human-Computer Interaction, and Artificial Intelligence, we are aiming at the new and growing markets for these technologies, such as Business, Economy, Market Research, and Medical and Health Care. The shift towards this new focus will be marked by a series of special issues covering hot and emergent topics.
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