Name
Title | ||
|---|---|---|
SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos. |
Abstract | ||
|---|---|---|
Micro-expression is a subtle and involuntary facial expression that may reveal the hidden emotion of human beings. Spotting micro-expression means to locate the moment when the micro-expression happens, which is a primary step for micro-expression recognition. Previous work in micro-expression spotting focus on spotting micro-expression from short video, and with hand-crafted features. In this paper, we present a methodology for spotting micro-expression from long videos. Specifically, a new convolutional neural network named spotting micro-expression convolutional network was designed for extracting features from video clips, which is the first time that deep learning is used in micro-expression spotting. Then, a feature matrix processing method was proposed for spotting the apex frame from long video, which uses a sliding window and takes the characteristics of micro-expression into account to search the apex frame. Experimental results demonstrate that the proposed method can achieve a better performance than the existing state-of-art methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2879485 | IEEE ACCESS |
Keywords | Field | DocType |
Spotting micro-expression,apex frame,convolutional neural network,deep learning | Computer vision,Microsoft Windows,Sliding window protocol,Computer science,Convolutional neural network,Feature extraction,Facial expression,Artificial intelligence,Feature matrix,Deep learning,Spotting,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 1 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihao Zhang | 1 | 9 | 3.49 |
| Tong Chen | 2 | 2 | 0.71 |
| Hongying Meng | 3 | 832 | 69.39 |
| Guangyuan Liu | 4 | 13 | 3.40 |
| Xiaolan Fu | 5 | 786 | 60.72 |