Abstract
The paper presents improvements in face identification performance using synthesized images as a perturbation method. Three facial expression features, smiles, anger and screams, were extracted from images of actual facial expression using the eigenspace method. Synthesized facial images based on these features were added to learning data of a personal identification model using support vector machines (SVM). The performance of this model was significantly higher than that of a model trained without facial expression images, but significantly lower than that of a model using actual expression images. The results suggest that identification performance also depends significantly on facial expression.
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Nakayama, M., Kumakura, T. (2005). Face Identification Performance Using Facial Expressions as Perturbation. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_87
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DOI: https://doi.org/10.1007/11550822_87
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