KENDALL-ROFT: Kendall’s Shape Analysis with Rigid Transformation and Optical Flow for Transformation-Based Micro-expression Recognition

Arafet Sbei, Hassen Drira, Faten Chaieb

Abstract: Micro-expressions are brief muscle changes that occur when people hide true emotions, making them hard to detect. Most existing approaches are pixel-based, which may fall short when external conditions such as lighting obscure the true expressions. To this end, a novel framework for micro-expression recognition called KENDALL-ROFT is proposed. It integrates: (1) a manifold-based approach leveraging Kendall’s shape space to capture subtle non-rigid deformations from 3D facial landmark configurations, (2) optical flow to highlight fine-grained facial displacements, and (3) rigid transformation parameters for global alignment. By embedding 3D facial landmarks into a pre-shape space and representing their differences via tangent vectors, our method isolates shape-specific variations from large-scale head movements. Optical flow, particularly when magnified, further accentuates transient muscle activations characteristic of micro-expressions, while rigid transformation (rotation, scale, translation) ensures reliable separation of overall head motion. Evaluations on CASME2, CAS(ME)3, and SAMM datasets demonstrate consistent performance gains in both simple and complex recognition scenarios, reaching state-of-the-art performance. These results show the importance of integrating geometry-aware shape representations, fine-motion cues, and global alignment as a robust and unified architecture to micro-expression recognition. Code is available at https://github.com/ROFT1/KENDALL-ROFT.

External IDs:doi:10.1007/978-3-032-07343-3_50