Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Feature-based image metamorphosis
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Realistic modeling for facial animation
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Synthesizing realistic facial expressions from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A morphable model for the synthesis of 3D faces
Proceedings of the 26th 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
AFRIGRAPH '01 Proceedings of the 1st international conference on Computer graphics, virtual reality and visualisation
Shape Detection in Computer Vision Using the Hough Transform
Shape Detection in Computer Vision Using the Hough Transform
Morphological Interpolation and Color Images
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
ACM SIGGRAPH 2006 Papers
ACM Transactions on Accessible Computing (TACCESS)
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Based on artistic methods used for manipulating perception, we present a technique that creates facial images with conflicting emotional states at different spatial frequencies. The foveal and peripheral components of the human visual system tend to interpret emotional states differently, adding a degree of elusiveness to the facial image. Our technique first isolates the coarser low spatial frequency components and finer high spatial frequency details from two images with differing facial expressions. We then perform image segmentation with edge detection, and morph the images. In practice we have found that high spatial frequency elements determine the dominant expression in the resulting image, while the low spatial frequency elements contribute subtlety.