Synthesizing realistic facial expressions from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Expressive expression mapping with ratio images
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Geometry-driven photorealistic facial expression synthesis
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Journal of Cognitive Neuroscience
Steerable pyramid-based face hallucination
Pattern Recognition
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As an important human characteristic, facial expression plays an important role in the applications of identity authentication, animation production, human-computer interaction, web-based education, etc. In this paper, we propose a facial expression hallucination approach through eigen-associative learning (EAL). The approach consists of two steps, in the first step, the global facial expression is estimated, and in the second step, we synthesize high-frequency image features to enhance the global face. The proposed EAL approach is adopted in both steps, which can synthesize the imaginary facial expressions of the input face with neutral expression. Compared with existing method, the EAL approach can be easily applied to new test data and retain high computational efficiency. Experiments show that the EAL approach generates reasonable imaginary facial expressions.