Micro-Expression Spotting with Face Alignment and Optical Flow

Facial expression spotting holds significant importance as it can signify emotional changes. Particularly, micro-expressions possess the potential to reveal genuine emotions, making them even more valuable in practical domains such as public safety and finance. However, spotting micro-expressions proves challenging due to their subtle movements and brief duration. This paper proposes an expression spotting method based on face alignment and optical flow. We first use a finer crop-align technique to preprocess the facial videos by aligning the face and the nose tip. Then, regions of interest (ROIs) are defined by analyzing the statistics of action units. The optical flow features are then extracted and subjected to lowpass filtering to eliminate high-frequency noise. Furthermore, candidate expression segments are identified based on the magnitude of the processed optical flows. Finally, non-maximum suppression is utilized to remove overlapping segments. The effectiveness of the proposed method is evaluated on the challenge test set, resulting in an overall F1-score of 0.19. Additional results obtained from CAS(ME)2 and SAMM Long videos provide further verification of the method's efficacy. The code is available online.