Models, code, and papers for "Sujing Wang":
This paper presents baseline results for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) on SAMM and CAS(ME)2. The proposed LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The performance is evaluated by Leave-One-Subject-Out cross validation. Furthermore, we define the criteria of determining true positives in one video by overlap rate and set the metric F1-score for spotting performance of the whole database. The F1-score of baseline results for SAMM and CAS(ME)2 are 0.0316 and 0.0179, respectively.
Recently, there are increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works usually used spatiotemporal local binary pattern for micro-expression analysis. However, the commonly used spatiotemporal local binary pattern considers dynamic texture information to represent face images while misses the shape attribute of face images. On the other hand, their works extracted the spatiotemporal features from the global face regions, which ignore the discriminative information between two micro-expression classes. The above-mentioned problems seriously limit the application of spatiotemporal local binary pattern on micro-expression recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an improved integral projection to resolve the problems of spatiotemporal local binary pattern for micro-expression recognition. Firstly, we develop an improved integral projection for preserving the shape attribute of micro-expressions. Furthermore, an improved integral projection is incorporated with local binary pattern operators across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition. Intensive experiments are conducted on three availably published micro-expression databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.