| Abstract | Illumination conditions have an explicit effect on the performance of face recognition systems. In
particular, varying the illumination condition upon the face imposes such complex effects that the identification
often fails to provide a stable performance level. In this paper, we propose an approach integrating
face identity and illumination models in order to reach acceptable and stable face recognition rates. For
this purpose, Active Appearance Model (AAM) and texture model of faces are combined in order to obtain
an illumination invariant face localization. The proposed method is an integrated Active Illumination and
Appearance Model (AIA) which combines identity, illumination and shape components in a single model
and allows us to control them, separately. One of the major advantage of the proposed AIA model is
that efficient model fitting is achieved, whilst keeping the performance against illumination changes. In
addition to model fitting, images illuminated from different directions can easily be synthesized by changing
the parameters related to illumination modes. The method provides a practical approach, since only
one image with frontal illumination of each person for training, is sufficient. There is no need to build
complex models for illumination. As a result, this paper has presented a simple and efficient method
for face modeling and face alignment in order to increase the performance of face localization by means
of the proposed illumination invariant AIA method for face alignment, such as the Active Appearance
Models, invariant to changes in illumination. From the experimental results, we showed that the proposed
AIA model provides higher accuracy than classical Active Appearance Model for face alignment in
a point-to-point error sense. |