What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Separating Style and Content with Bilinear Models
Neural Computation
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Incremental update of linear appearance models and its application to AAM: incremental AAM
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Active Appearance Model is a well-known model that can represent a non-rigid object effectively. However, since it uses the fixed appearance model, the fitting results are often unsatisfactory when the imaging condition of the target image is different from that of training images. To alleviate this problem, incremental AAM was proposed which updates its appearance bases in an on-line manner. However, it can not deal with the sudden changes of illumination. To overcome this, we propose a novel scheme to update the appearance bases. When a new person appears in the input image, we synthesize illuminated images of that person and update the appearance bases of AAM using it. Since we update the appearance bases using synthesized illuminated images in advance, the AAM can fit their model to a target image well when the illumination changes drastically. The experimental results show that our proposed algorithm improves the fitting performance over both the incremental AAM and the original AAM.