Human Facial Expression Recognition Using Stepwise Linear Discriminant Analysis and Hidden Conditional Random Fields
- 24 February 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 24 (4), 1386-1398
- https://doi.org/10.1109/tip.2015.2405346
Abstract
This paper introduces an accurate and robust facial expression recognition (FER) system. For feature extraction, the proposed FER system employs stepwise linear discriminant analysis (SWLDA). SWLDA focuses on selecting the localized features from the expression frames using the partial F-test values, thereby reducing the within class variance and increasing the low between variance among different expression classes. For recognition, the hidden conditional random fields (HCRFs) model is utilized. HCRF is capable of approximating a complex distribution using a mixture of Gaussian density functions. To achieve optimum results, the system employs a hierarchical recognition strategy. Under these settings, expressions are divided into three categories based on parts of the face that contribute most toward an expression. During recognition, at the first level, SWLDA and HCRF are employed to recognize the expression category; whereas, at the second level, the label for the expression within the recognized category is determined using a separate set of SWLDA and HCRF, trained just for that category. In order to validate the system, four publicly available data sets were used, and a total of four experiments were performed. The weighted average recognition rate for the proposed FER approach was 96.37% across the four different data sets, which is a significant improvement in contrast to the existing FER methods.Keywords
Funding Information
- National Research Foundation of Korea within the Korea Government through the Ministry of Science, ICT and Future Planning (2013-067321)
- Industrial Strategic Technology Development Program through the Ministry of Knowledge Economy, Korea (10035348)
This publication has 52 references indexed in Scilit:
- Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector MachinesSensors, 2013
- Facial Feature Extraction Based on Local Color and Texture for Face Recognition using Neural NetworkInternational Journal of Science and Engineering Applications, 2013
- Facial Expression Recognition Using Local Transitional Pattern on Gabor Filtered Facial ImagesIETE Technical Review, 2013
- A Neural Network based Facial Expression Recognition using FisherfaceInternational Journal of Computer Applications, 2012
- The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial ExpressionsPLOS ONE, 2012
- Robust Facial Expression Recognition Based on Local Directional PatternETRI Journal, 2010
- Facial expression recognition based on Local Binary Patterns: A comprehensive studyImage and Vision Computing, 2009
- The contribution of different facial regions to the recognition of conversational expressionsJournal of Vision, 2008
- Toward enhanced P300 speller performanceJournal of Neuroscience Methods, 2008
- Feature extraction and dimensionality reduction algorithms and their applications in vowel recognitionPattern Recognition, 2003