Abstract
Dictionary learning has attracted growing intention for its prominent performance in many computer vision applications including facial expression recognition (FER). Discriminative K-SVD (D-KSVD) is one of conventional dictionary learning methods, which can effectively unify dictionary learning and classifier. However, the computation is huge when applying D-KSVD directly on Gabor features which has high dimension. To tackle this problem, we employ random projection on Gabor features and then put the reduced features into D-KSVD schema to obtain sparse representation and dictionary. To evaluate the performance, we implement the proposed method for FER on JAFFE database. We also employ support vector machine (SVM) on the sparse codes for FER. Experimental results show that the computation is reduced a lot with little performance lost.
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Acknowledgments
This paper is supported by the National Nature Science Foundation of China (No. 61271407), the Nature Science Foundation of Shandong Province (No. ZR2011FQ016) and the Fundamental Research Funds for the Central Universities (No. 13CX02096A).
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Liu, W., Song, C., Wang, Y. (2013). Efficient Discriminative K-SVD for Facial Expression Recognition. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38466-0_2
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DOI: https://doi.org/10.1007/978-3-642-38466-0_2
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