Abstract
Expression recognition is an important subjective measurement method in emotional calculation. Considering the stability and representativeness of image algebra features, expression characteristics can be extracted by singular value decomposition. BP neural network optimized by the genetic algorithm is adopted as a classifier. Using the classifier, an expression recognition experiment was done on the JAFFE library and emotional induced experimental expression database. Comparing with the traditional BP classifier, the results of the experiment proves that the method is more effective and efficient.
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Qin, W., Fang, Q., Yang, Y. (2013). A Facial Expression Recognition Method Based on Singular Value Features and Improved BP Neural Network. In: Tan, T., Ruan, Q., Chen, X., Ma, H., Wang, L. (eds) Advances in Image and Graphics Technologies. IGTA 2013. Communications in Computer and Information Science, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37149-3_20
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DOI: https://doi.org/10.1007/978-3-642-37149-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37148-6
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