Multi-scale active patches fusion based on spatiotemporal LBP-TOP for micro-expression recognition

摘要

Micro-expressions are spontaneous emotions appearing on a face that is hard to conceal and thus making them different from normal facial expressions both in duration and subtlety. This paper investigates a challenging issue in micro-expression, where not all facial regions contribute equally to effective representation. Consequently, we proposed a multi-scale active patches fusion-based spatiotemporal LBP-TOP descriptor that considers the active contributions for different region area in faces. For the feature procedure, we exploit the average value of all patches under each scale to obtain the threshold that selectively fuses the local and global features. On the other hand, an improved weighted sparse representation based dual augmented Lagrange multiplier is adopted for the classification to remit the problem of sparse coefficients obtained by the traditional sparse representation algorithm. We conduct comprehensive experiments on CASME II and SAMM datasets and the accuracies respectively reach 77.30% and 58.82% using LOSO cross-validation. (C) 2020 Elsevier Inc. All rights reserved.