A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challenges

Abstract

Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was largely manual. Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.

45 pages, single column preprint version. Submitted: 2 December 2017, Accepted: 12 June 2018 to Frontiers in Psychology

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• Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition
• Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining
• Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition
• Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition
• Region attention and graph embedding network for occlusion objective class-based micro-expression recognition