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• W2121002029 abstract "Facial images captured by camera are usually influenced by several different kinds of factor, such as identity, expression, and etc. Therefore expressive image analysis plays an important role in many different applications, such as recognition, face recognition with expressive images, expressive image synthesis and etc. In this paper, human identity and facial expression are regarded as two influence factors of face appearance, and a novel kernel-based bilinear factorization method is proposed to decouple the interaction from each other. The trained non-linear factorization model has been used for expressive image translation, and to person and/or recognition, and got promising results. In this paper, the principle of our method is described and some preliminary experimental results are included." @default.
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• W2121002029 date "2005-03-31" @default.
• W2121002029 modified "2023-09-25" @default.
• W2121002029 title "Facial Expressive Image Analysis by Using Nonlinear Factorization Model" @default.
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• W2121002029 doi "https://doi.org/10.1109/icig.2004.64" @default.
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