Next Article in Journal
Real-Time Straight-Line Detection for XGA-Size Videos by Hough Transform with Parallelized Voting Procedures
Previous Article in Journal
A Ring Artifact Correction Method: Validation by Micro-CT Imaging with Flat-Panel Detectors and a 2D Photon-Counting Detector
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(2), 275;

Active AU Based Patch Weighting for Facial Expression Recognition

Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
Author to whom correspondence should be addressed.
Received: 30 December 2016 / Accepted: 24 January 2017 / Published: 30 January 2017
(This article belongs to the Section Sensor Networks)
PDF [2656 KB, uploaded 30 January 2017]


Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn–Kanade (CK+) databases, respectively. Better cross-database performance has also been observed. View Full-Text
Keywords: expression recognition; expression triplet; feature optimization; AU weighting; active AU detection expression recognition; expression triplet; feature optimization; AU weighting; active AU detection

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Xie, W.; Shen, L.; Yang, M.; Lai, Z. Active AU Based Patch Weighting for Facial Expression Recognition. Sensors 2017, 17, 275.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top