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

Algorithm 1

SRC-SM-SV algorithm

Input: labeled training data , regularization parameters ,,,, and .

Output: the optimal variables {}

1. Initialize the dictionary using K-SVD algorithm

While not convergence or

2. Compute the similarity matrix via Equation (4);

3. Tune the mapping matrix via Equations (10)–(15);

4. Tune the dictionary via Equation (17);

5. Tune sparse coefficient matrix via Equation (20);

6. Tune the via Equation (21);

end while