Synthesis of High-Resolution Facial Image Based on Top-Down Learning
This paper proposes a method of synthesizing a high-resolution facial image from a low-resolution facial image based on top-down learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in an given low-resolution facial image, we can estimate optimal coeficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be synthesized by using the optimal coeficients for linear combination of the high-resolution prototypes. The encouraging results of the proposed method show that our method can be used to increase the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at surveillance systems.
KeywordsFace Recognition Facial Image Input Face Bicubic Interpolation Reference Face
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- Beymer, D., Shashua, A., Poggio, T.: Example-Based Image Analysis and Synthesis. AI Memo 1431/CBCL Paper 80, Massachusetts Institute of Technology, Cambridge, MA (Nov. 1993)Google Scholar
- Blanz, V., Romdhani, S., Vetter, T.: Face Identification across Different Poses and Illuminations with a 3D Morphable Model. Proc. of the 5th Int’l Conf. on Automatic Face and Gesture Recognition, Washington, D. C. (2002) 202–207Google Scholar