Improving the Robustness of Subspace Learning Techniques for Facial Expression Recognition

2010 ◽  
pp. 470-479 ◽  
Author(s):  
Dimitris Bolis ◽  
Anastasios Maronidis ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Subspace Learning ◽  
Expression Recognition ◽  
Learning Techniques

scholarly journals Robust Transferable Subspace Learning for Cross-Corpus Facial Expression Recognition

2020 ◽  
Vol E103.D (10) ◽  
pp. 2241-2245
Author(s):  
Dongliang CHEN ◽  
Peng SONG ◽  
Wenjing ZHANG ◽  
Weijian ZHANG ◽  
Bingui XU ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Subspace Learning ◽  
Expression Recognition

Combining the Kernel Collaboration Representation and Deep Subspace Learning for Facial Expression Recognition

2018 ◽  
Vol 27 (08) ◽  
pp. 1850121 ◽  
Author(s):  
Zhe Sun ◽  
Zheng-Ping Hu ◽  
Raymond Chiong ◽  
Meng Wang ◽  
Wei He
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Subspace Learning ◽  
Superior Performance ◽  
Expression Recognition ◽  
Learning Networks ◽  
Linear Discriminant ◽  
High Level ◽  
Relationship Of

Recent research has demonstrated the effectiveness of deep subspace learning networks, including the principal component analysis network (PCANet) and linear discriminant analysis network (LDANet), since they can extract high-level features and better represent abstract semantics of given data. However, their representation does not consider the nonlinear relationship of data and limits the use of features with nonlinear metrics. In this paper, we propose a novel architecture combining the kernel collaboration representation with deep subspace learning based on the PCANet and LDANet for facial expression recognition. First, the PCANet and LDANet are employed to learn abstract features. These features are then mapped to the kernel space to effectively capture their nonlinear similarities. Finally, we develop a simple yet effective classification method with squared [Formula: see text]-regularization, which improves the recognition accuracy and reduces time complexity. Comprehensive experimental results based on the JAFFE, CK[Formula: see text], KDEF and CMU Multi-PIE datasets confirm that our proposed approach has superior performance not just in terms of accuracy, but it is also robust against block occlusion and varying parameter configurations.

Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets

2011 ◽  
Vol 24 (8) ◽  
pp. 814-823 ◽  
Author(s):  
Anastasios Maronidis ◽  
Dimitris Bolis ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Subspace Learning ◽  
Expression Recognition ◽  
Training Sets

Image Processing and Machine Learning Techniques for Facial Expression Recognition

2009 ◽  
pp. 247-262 ◽  
Author(s):  
Anastasios Koutlas ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Expression Recognition ◽  
Basic Emotions ◽  
Learning Techniques ◽  
The Face

The aim of this chapter is to analyze the recent advances in image processing and machine learning techniques with respect to facial expression recognition. A comprehensive review of recently proposed methods is provided along with an analysis of the advantages and the shortcomings of existing systems. Moreover, an example for the automatic identification of basic emotions is presented: Active Shape Models are used to identify prominent features of the face; Gabor filters are used to represent facial geometry at selected locations of fiducial points and Artificial Neural Networks are used for the classification into the basic emotions (anger, surprise, fear, happiness, sadness, disgust, neutral); and finally, the future trends towards automatic facial expression recognition are described.

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