.png){kind=icon}
.png){kind=icon}
Return
Manifold feature integration for micro-expression recognition
.png){kind=icon}
PRE
AI
DOI：10.1007/s00530-020-00663-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recognition of micro-expressions depends on the key features provided in the form of the temporal information. It needs considerable effort, however, to manually design useful characteristics. Subtle or micro-facial expressions are much difficult than regular facial expressions rich in emotional expressions in a true environment to be identified. An easy solution is discussed in this paper to recognise facial micro-expressions that utilizes an algorithm mix for facial identification, feature extraction and classification. The technique proposed is a framework which incorporates handcrafted features and deep features. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) is the handcraft feature which combines spatial and time analysis to encapsulate regional facet movements. The deep feature model is a micro-expression fine-tuned model based on Convolutional Neural Network (CNN). Two classifiers, i.e. SVM and Softmax are trained with combined feature vectors produced by LBP-TOP and CNN functionalities. All seven widely-used micro-expression databases are evaluated in an experiment. Our research can be claimed as the first extensive experimental study on a big amount of the datasets to train and test the suggested model. The findings in the document show that the method proposed, although simple and straightforward, achieves a substantial increase in precision relative to other commonly recognized micro-expression techniques, which are trained and tested with just a few datasets.
Keywords:
Micro-expression recognition
Data augmentation
Fine-tuning
Local Binary Pattern-Three Orthogonal Planes (LBP-TOP)
Convolutional neural networks (CNN)
Manifold feature learning and integration
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.1
Papers:
2.7K
Citations:
2.7K