Micro-expression (ME) is a significant non-verbal communication clue that reveals one person's genuine emotional state. The development of micro-expression analysis (MEA) has just gained attention in the last decade. However, the small sample size problem constrains the use of deep learning on MEA. Besides, ME samples distribute in six different databases, leading to database bias. Moreover, the ME database development is complicated. In this article, we introduce a large-scale spontaneous ME database: CAS(ME) $^{3}$ . The contribution of this article is summarized as follows: (1) CAS(ME) $^{3}$ offers around 80 hours of videos with over 8,000,000 frames, including manually labeled 1,109 MEs and 3,490 macro-expressions. Such a large sample size allows effective MEA method validation while avoiding database bias. (2) Inspired by psychological experiments, CAS(ME) $^{3}$ provides the depth information as an additional modality unprecedentedly, contributing to multi-modal MEA. (3) For the first time, CAS(ME) $^{3}$ elicits ME with high ecological validity using the mock crime paradigm, along with physiological and voice signals, contributing to practical MEA. (4) Besides, CAS(ME) $^{3}$ provides 1,508 unlabeled videos with more than 4,000,000 frames, i.e., a data platform for unsupervised MEA methods. (5) Finally, we demonstrate the effectiveness of depth information by the proposed depth flow algorithm and RGB-D information.

中文翻译：

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CAS(ME)3：具有深度信息和高生态有效性的第三代面部自发微表情数据库

微表情（ME）是一种重要的非语言交流线索，可以揭示一个人的真实情绪状态。微表达分析（MEA）的发展在过去十年才引起人们的关注。然而，小样本问题限制了深度学习在 MEA 上的使用。此外，ME样本分布在6个不同的数据库中，导致数据库偏差。而且ME数据库开发比较复杂。在本文中，我们介绍一个大规模自发ME数据库：CAS(ME) $^{3}$ 。本文的贡献总结如下： (1) CAS(ME) $^{3}$提供约 80 小时的视频，超过 8,000,000 帧，包括手动标记的 1,109 个 ME 和 3,490 个宏表达式。如此大的样本量可以有效验证 MEA 方法，同时避免数据库偏差。(2) 受心理学实验启发，CAS(ME) $^{3}$前所未有地提供深度信息作为附加模态，为多模态 MEA 做出贡献。(3) 首次获得CAS(ME) $^{3}$使用模拟犯罪范式以及生理和语音信号引出具有高生态有效性的 ME，为实用的 MEA 做出贡献。(4)此外，CAS(ME) $^{3}$提供1,508个超过4,000,000帧的无标签视频，即无监督MEA方法的数据平台。(5)最后，我们通过所提出的深度流算法和RGB-D信息证明了深度信息的有效性。