Matching expression variant faces.

Several models have been proposed that attempt to explain how the brain identifies people by looking at their faces. However, to date, it is still not clear by which mechanism the brain successfully accomplishes the matching of two or more face images when differences in facial expression make the (local and global) appearance of these images different from one another. There seems to be a consensus that faces are processed holistically rather than locally, but there is not yet consensus on whether information on facial expression is passed to the identification process to aid recognition of individuals or not. Models have been proposed that exploit each of these two views, and psychophysical data exist in favor of and against each view. In this article, we show how the experimental data of these two opposite views can be explained by incorporating a key process of motion estimation in the classical feedforward model of face processing. This new model will then lead us to hypothesize that to successfully match expression variant faces, it is convenient to use the information supplied by this motion estimation process within the matching task. We will show experimental results in favor of this hypothesis. Finally, we will show how we can also use the same motion estimator to recognize facial expressions.