An Improved Statistical Model of Appearance under
Partial Occlusion
1Qaisar Abbas and 2Tanzila Saba
1College of Computer and Information Sciences, Al Imam
Muhammad Ibn Saud Islamic University, Saudi Arabia
2College
of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
Abstract: The Appearance Models (AMs) are widely
used in many applications related to face recognition, expression analysis and
computer vision. Despite its popularity, the AMs are not much more accurate due
to partial occlusion. Therefore, the authors have developed Robust
Normalization Inverse Compositional Image Alignment (RNICIA) algorithm to solve
partial occlusion problem. However, the RNICIA algorithm is not efficient due
to high complexity and un-effective due to poor selection of Robust Error
Function and scale parameter that depends on a particular training dataset. In
this paper, an Improved Statistical Model of Appearance (ISMA) method is
proposed by integration techniques of perceptual-oriented uniform Color
Appearance Model (CAM) and Jensen-Shannon Divergence (JSD) to overcome these
limitations. To reduce iteration steps which decrease computational complexity,
the distribution of probability of each occluded and un-occluded image regions
is measured. The ISMA method is tested by using convergence measure on 600
facial images by varying degree of occlusion from 10% to 50%. The experimental
results indicate that the ISMA method is achieved more than 95% convergence compared
to RNICIA algorithm thus the performance of appearance models have
significantly improved in terms of partial occlusion.
Keywords: Computer vision, appearance model,
partial occlusion, robust error functions, CIECAM02 appearance model.