Abstract
We present a sample-based method for synthesizing face images in a wide range of view. Here the ”human identity” and ”head pose” are regarded as two influence factors of face appearance and a factorization model is used to learn their interaction with a face database. Our method extends original bilinear factorization model to nonlinear case so that global optimum solution can be found in solving ”translation” task. Thus, some view of a new person’s face image is able to be ”translated” into other views. Experimental results show that the synthesized faces are quite similar to the ground-truth. The proposed method can be applied to a broad area of human computer interaction, such as face recognition across view or face synthesis in virtual reality.
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Du, Y., Lin, X. (2004). Multi-View Face Image Synthesis Using Factorization Model. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. CVHCI 2004. Lecture Notes in Computer Science, vol 3058. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24837-8_19
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DOI: https://doi.org/10.1007/978-3-540-24837-8_19
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