A weighted feature extraction method based on temporal accumulation of optical flow for micro-expression recognition

Micro-expression Video Clip Synthesis Method based on Spatial-temporal Statistical Model and Motion Intensity Evaluation Function

“…The results in Table III show that all the above data augmentation approaches improve ME recognition performance. The best accuracy is 69.72%, which is obtained from the enrichment by our generated samples using the recognition method in [14]. The recognition accuracy of the enrichment by our generated samples is 3.77% higher than that of no augmentation.…”

“…Video clips in CASME II consists of seven classes of micro-expressions: surprise, happiness, fear, repression, disgust, sadness, and others. In our experiments, we classify CASME II into four categories: negative, positive, surprise, and others as [14]. The specific emotions that each class contains and the number of clips in CASME II are shown in Table I.…”

“…Here, we employ the methods in [14] [15], which are two of the state of the art methods, for ME recognition. Both methods extract LBP-TOP features from ME database first, then utilizes SVM to classify MEs.…”

“…Hence, we compare ME recognition rates of our approach to these two augmentation approaches. As our method concentrates on MEs' dynamic information rather than texture information when generating new ME samples, here we employ two state-of-the-art ME recognition methods [14], [15] which pay more attention to dynamic information when recognizing MEs. The recognition results are shown in Table III.…”

“…5 and Fig. 6 show the confusion matrices of no augmentation, augmentation by adding noise, augmentation by image flipping, and augmentation by our method for weighted LBP-TOP [14] and LBP-IP [15] respectively. As shown in Fig.…”