Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Animation of Synthetic Faces in MPEG-4
CA '98 Proceedings of the Computer Animation
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Facial Expression Decomposition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Statistical synthesis of facial expressions for the portrayal of emotion
Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
MPEG-4 facial animation technology: survey, implementation, and results
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a methodology to synthesize facial expressions from photographs for devices with limited processing power, network bandwidth and display area, which is referred as ''LLL'' environment. The facial images are reduced to small-sized face alive icons (FAI). Expressions are decomposed into the expression-unrelated facial features and the expression-related expressional features. As a result, the common features can be identified and reused across expressions using a discrete model constructed from the statistical analysis on training dataset. Semantic synthesis rules are introduced to reveal the inner relations of expressions. Verified by the experimental prototype system and usability study, the approach can produce acceptable facial expression images utilizing much less computing, network and storage resource than the traditional approaches.