Date post: | 12-Dec-2014 |
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BY
B .Srikanth08Q61A0562
Department
Of
Computer Science
AVANTHI INSTITUTE OF ENGINEERING & TECHNOLOGY
Face-Morphing
Introduction
“Morphing” is an interpolation technique used to create a series of intermediate objects from two objects.
“The face-morphing” automatically extracts feature points on the face and morphing is performed.
This was proposed by Mr. M.Biesel within Bayesian framework to do automatic face morphing.
Outline of the face-morphing
Pre – Processing
removing the noisy backgrounds
clipping to get a proper facial image, and
scaling the image to a reasonable size.
Feature Finding
We first compute the complexity map we multiply the complexity map by a weighting function. we find the three highest peaks in the weighted complexity
map . Finally, the similarity is measured in the correlation-coefficient
sense
Here we find 4 major feature points, namely the two eyes, and the two end-points of the mouth.
Feature Finding
Image PartitioningFeature finder can gives the positions of the eyes and the end points of the mouth, so we get 4 feature points.
We generate 6 more feature points around the face edge.
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Contd….
Based on these 10 feature points, 16 non-overlapping triangular or quadrangular regions developed
As the feature points are at different positions, we do warping between images.
Image1 Image2
Image Partitioning
Coordinate TransformationsAffine Transformation
DA
B
C EF
pq
Coordinate TransformationsBilinear Transformation
Cross-DissolvingThe last phase of face - morphing
Cross-dissolving is described by the following equation,
where A,B are the pair of images, and C is the morphing result.
Example on typical morphing process.
__________
Pre - Processing
Coordinate transformations
__________
Cross-dissolve
Morphing between faces of different people - human and animal (lion)
- man and man
Morphing between different images of the same person
Serious <=== ===> Smiling
Conclusion We believe that feature extraction is the key
technique for automatic face morphing.
More and more feature points give the better result
Therefore, in this presentation we emphasized on eye-finder.
This method, is successful in eye detection rate by 84%.
References
Martin Bichsel, "Automatic Interpolation and Recognition of Face Images by Morphing”.
Jonas Gomes, "Warping and morphing of graphical objects", Morgan Kaufmann Publishers (1999).