定位
任务(项目管理)
表达式(计算机科学)
水准点(测量)
计算机科学
代表(政治)
学习迁移
机器学习
人工智能
模式识别(心理学)
定位关键字
程序设计语言
经济
地理
法学
管理
大地测量学
政治
政治学
作者
Gen-Bing Liong,John See,Chee-Seng Chan
标识
DOI:10.1016/j.image.2022.116875
摘要
Facial Micro-Expressions (MEs) reveal a person’s hidden emotions in high stake situations within a fraction of a second and at a low intensity. The broad range of potential real-world applications that can be applied has drawn considerable attention from researchers in recent years. However, both spotting and recognition tasks are often treated separately. In this paper, we present Micro-Expression Analysis Network (MEAN), a shallow multi-stream multi-output network architecture comprising of task-specific (spotting and recognition) networks that is designed to effectively learn a meaningful representation from both ME class labels and location-wise pseudo-labels. Notably, this is the first known work that addresses ME analysis on long videos using a deep learning approach, whereby ME spotting and recognition are performed sequentially in a two-step procedure: first spotting the ME intervals using the spotting network, and proceeding to predict their emotion classes using the recognition network. We report extensive benchmark results on the ME analysis task on both short video datasets (CASME II, SMIC-E-HS, SMIC-E-VIS, and SMIC-E-NIR), and long video datasets (CAS(ME)2 and SAMMLV); the latter in particular demonstrates the capability of the proposed approach under unconstrained settings. Besides the standard measures, we promote the usage of fairer metrics in evaluating the performance of a complete ME analysis system. We also provide visual explanations of where the network is “looking” and showcasing the effectiveness of inductive transfer applied during network training. An analysis is performed on the in-the-wild dataset (MEVIEW) to open up further research into real-world scenarios.
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