Paper Info
Title
Recognizing hidden emotions from difference image using mean local mapped pattern
Abstract
Recent progress in computer vision has pushed the limit of facial recognition from human identification to micro-expressions (MEs). However, the visual analysis of MEs is still a very challenging task because of the short occurrence and insignificant intensity of the underlying signals. To date, the accuracy of recognizing hidden emotions from frames using conventional methods is still far from reaching saturation. To address this, we have proposed a new ME recognition approach based on Mean Local Mapped Pattern (M-LMP) as a texture feature, which outperforms other state-of-the art features in terms of accuracy due to its capability of capturing small pixel transitions. Inspired by previous work, we applied M-LMP to the difference image computed from an onset frame and an apex frame, where the former represents the frame with neutral emotion and the latter consists of the frame with the largest ME intensity. The extracted local features were classified using support vector machine (SVM) and K nearest neighbourhood (KNN) classifiers. The validation of the proposed approach was performed on the CASME II and CAS(ME)2 datasets, and the results were compared with other similar state-of-the-art approaches. Comprehensive experiments were conducted using various parameters to show the robustness of our approach in the imbalanced and small dataset.
Year
DOI
Venue
2019
10.1007/s11042-019-7385-y
Multimedia Tools and Applications
Keywords
Field
DocType
CASME II, KNN, Micro-expressions, M-LMP, SVM
Computer vision,Facial recognition system,Pattern recognition,Computer science,Support vector machine,Robustness (computer science),Pixel,Artificial intelligence
Journal
Volume
Issue
ISSN
78
15
1380-7501
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Kam Meng Goh131.72
Usman Ullah Sheikh2498.41
T. H. Maul3176.41