Facial expression has a significant role in analyzing human cognitive state. Deriving an accurate facial appearance representation is a critical task for an automatic facial expression recognition application. This paper provides a new feature descriptor named as Quadrilateral Senary bit Pattern for facial expression recognition. The QUEST pattern encoded the intensity changes by emphasizing the relationship between neighboring and reference pixels by dividing them into two quadrilaterals in a local neighborhood. Thus, the resultant gradient edges reveal the transitional variation information, that improves the classification rate by discriminating expression classes. Moreover, it also enhances the capability of the descriptor to deal with viewpoint variations and illumination changes. The trine relationship in a quadrilateral structure helps to extract the expressive edges and suppressing noise elements to enhance the robustness to noisy conditions. The QUEST pattern generates a six-bit compact code, which improves the efficiency of the FER system with more discriminability. The effectiveness of the proposed method is evaluated by conducting several experiments on four benchmark datasets: MMI, GEMEP-FERA, OULU-CASIA, and ISED. The experimental results show better performance of the proposed method as compared to existing state-art-the approaches.
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors
Fasa University（法萨大学）；Deakin University（迪肯大学）；University of Tabriz（大不里士大学）；Universidad de Granada（格拉纳达大学）；University of Cambridge（剑桥大学）；Harvard University（哈佛大学）
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
Dept. of Computer Science & Engineering, University of Notre Dame, USA（圣母大学计算机科学与工程系，美国）；Faculty of Electrical Engineering, University of Ljubljana, Slovenia（卢布尔雅那大学电气工程学院，斯洛文尼亚）
Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond
The Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）；Tencent（腾讯）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）；Westlake University（西湖大学）
On the Illusion of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes
Paderborn University（帕德博恩大学）；Technical University of Darmstadt（达姆施塔特技术大学）；University of Notre Dame（诺特丹大学）；Johannes Gutenberg University Mainz（美因茨约翰· Gutenberg大学）
Expression Analysis Based on Face Regions in Read-world Conditions
Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Comparing Human and Machine Bias in Face Recognition
University of Maryland（马里兰大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Pomona College（波莫纳学院）；Howard University（霍华德大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；University of Georgia（佐治亚大学）；Haverford College（哈弗福德学院）
Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition
CHI – Chair of Health Informatics, MRI, Technical University of Munich（慕尼黑技术大学健康信息学系）；MCML – Munich Center for Machine Learning（慕尼黑机器学习中心）；GLAM – Group on Language, Audio, & Music（语言、音频与音乐小组）；MDSI – Munich Data Science Institute（慕尼黑数据科学研究所）
MEGC2026: Micro-Expression Grand Challenge on Visual Question Answering
Department of Computing and Mathematics, Manchester Metropolitan University（曼彻斯特 Metropolitan 大学计算与数学系）；State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, CAS & Department of Psychology, University of the Chinese Academy of Sciences（中国科学院心理研究所认知科学与心理健康国家重点实验室及中国科学院大学心理学系）；School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia（赫瑞-沃森大学马来西亚分校数学与计算机科学学院）
Your Face Mirrors Your Deepest Beliefs-Predicting Personality and Morals through Facial Emotion Recognition
MIT Center for Collective Intelligence（麻省理工学院集体智能中心）；University of Perugia（佩鲁贾大学）；Galaxyadvisors AG（Galaxyadvisors 公司）；Lucerne University of Applied Sciences and Arts（卢塞恩应用科学与艺术大学）；University of Cologne（科隆大学）
MEGC2025: Micro-Expression Grand Challenge on Spot Then Recognize and Visual Question Answering
Department of Computing and Mathematics, Manchester Metropolitan University（计算与数学系，曼彻斯特 Metropolitan 大学）；State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences（认知科学与心理健康国家重点实验室，心理学研究所，中国科学院）；Department of Psychology, University of the Chinese Academy of Sciences（心理学系，中国科学院大学）；National Taiwan University（台湾大学）；Zhejiang University（浙江大学）；University of Oulu（奥卢大学）；Harbin Institute of Technology（哈尔滨工业大学）
