scholarly journals A Modified Sparse Representation Method for Facial Expression Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Wang ◽  
LiHong Xu
Keyword(s):  
Facial Expression ◽  
Sparse Representation ◽  
Facial Expression Recognition ◽  
Matching Pursuit ◽  
Iteration Process ◽  
Orthogonal Matching Pursuit ◽  
Expression Recognition ◽  
Training Samples ◽  
Reduction Methods ◽  
Representation Method

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan’s JAFFE and CMU’s CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

scholarly journals Sparse Representation Classifier Embedding Subspace Mapping and Support Vector for Facial Expression Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaoqin Lu ◽  
Lei Xue ◽  
Xiaoqing Gu
Keyword(s):  
Facial Expression ◽  
Sparse Representation ◽  
Facial Expression Recognition ◽  
Structural Information ◽  
Classification Performance ◽  
Support Vector ◽  
Expression Recognition ◽  
Training Samples ◽  
Label Information ◽  
Sparse Representation Classifier

With the development of integration and innovation of Internet and industry, facial expression recognition (FER) technology is widely applied in wireless communication and mobile edge computing. The sparse representation-based classification is a hot topic in computer vision and pattern recognition. It is one type of commonly used image classification algorithms for FER in recent years. To improve the accuracy of FER system, this study proposed a sparse representation classifier embedding subspace mapping and support vector (SRC-SM-SV). Based on the traditional sparse representation model, SRC-SM-SV maps the training samples into a subspace and extracts rich and discriminative features by using the structural information and label information of the training samples. SRC-SM-SV integrates the support vector machine to enhance the classification performance of sparse representation coding. The solution of SRC-SM-SV uses an alternate iteration method, which makes the optimization process of the algorithm simple and efficient. Experiments on JAFFE and CK+ datasets prove the effectiveness of SRC-SM-SV in FER.

Sign in / Sign up

Export Citation Format

Share Document