Wang Yandan, See John, Phan Raphael C-W, Oh Yee-Hui
School of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang, China; Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia.
Faculty of Computing & Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia.
PLoS One. 2015 May 19;10(5):e0124674. doi: 10.1371/journal.pone.0124674. eCollection 2015.
Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datasets--SMIC and CASME II, both with baseline results released using the widely used dynamic texture descriptor LBP-TOP for feature extraction. Although LBP-TOP is popular and widely used, it is still not compact enough. In this paper, we draw further inspiration from the concept of LBP-TOP that considers three orthogonal planes by proposing two efficient approaches for feature extraction. The compact robust form described by the proposed LBP-Six Intersection Points (SIP) and a super-compact LBP-Three Mean Orthogonal Planes (MOP) not only preserves the essential patterns, but also reduces the redundancy that affects the discriminality of the encoded features. Through a comprehensive set of experiments, we demonstrate the strengths of our approaches in terms of recognition accuracy and efficiency.
微表情识别仍处于初级阶段，这在很大程度上归因于数据集开发过程中面临的诸多困难。由于微表情是临床诊断和欺骗分析的重要情感线索，因此人们为了研究目的在创建这些数据集方面付出了很多努力。目前有两个公开可用的自发微表情数据集——SMIC和CASME II，两者都发布了使用广泛使用的动态纹理描述符LBP-TOP进行特征提取的基线结果。尽管LBP-TOP很受欢迎且被广泛使用，但它仍然不够紧凑。在本文中，我们通过提出两种有效的特征提取方法，从考虑三个正交平面的LBP-TOP概念中获得了进一步的启发。所提出的LBP-六个交点（SIP）和超紧凑的LBP-三个平均正交平面（MOP）所描述的紧凑鲁棒形式不仅保留了基本模式，还减少了影响编码特征判别性的冗余。通过一系列全面的实验，我们在识别准确性和效率方面展示了我们方法的优势。
