Fitting Enhancement of AAM Using Synthesized Illumination Images

조명 영상 합성을 통한 AAM 피팅 성능 개선

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 cannot 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.