Active shape models to automatic morphing of face images
ISCGAV'04 Proceedings of the 4th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Active Shape Models and Evolution Strategies to Automatic Face Morphing
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Automatic morphing of face images
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Face morphing using 3D-aware appearance optimization
Proceedings of Graphics Interface 2012
Image morphing: transfer learning between tasks that have multiple outputs
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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This article presents a new method for generating an optimum image morph field, i.e. an optimum mapping of one image to another image by distorting the brightness and geometry of the former image. The mapping is calculated by maximizing the probability of the morph field in a Bayesian framework. In contrast to other techniques this new method needs no training and is derived based on group invariances and expected singularities, only. An infeasible exhaustive search is replaced by an new iterative approximation approach where, in a neighborhood around the current morph field solution, the probability distribution is approximated by a Gaussian probability distribution for which the most likely solution is evaluated by linear algebra techniques. New views are generated by applying linearly interpolated morph fields to the original reference image. Experiments demonstrate that this approach is well suited to interpolate between different views of a single face image or between images of different persons. Finally, a new face recognition algorithm makes use of the fact that morphs among images of a single person are confined to a five-dimensional subspace within the space of all possible morphs.