Recognizing Imprecisely Localized, Partially Occluded and Expression Variant Faces from a Single Sample per Class
IEEE Transactions on Pattern Analysis and Machine Intelligence, accepted
Abstract:
The classical way of attempting to solve the face (or object)
recognition problem is by using large and representative
datasets. In many applications though, only one sample per class
is available to the system. In this contribution, we describe a
probabilistic approach that is able to compensate for imprecisely
localized, partially occluded and expression variant faces even
when only one single training sample per class is available to
the system. To solve the localization problem, we find the
subspace (within the feature space, e.g. eigenspace) that
represents this error for each of the training images. To resolve
the occlusion problem, each face is divided into $k$ local
regions which are analyzed in isolation. In contrast with other
approaches, where a simple voting space is used, we present a
probabilistic method that analyzes how ``good" a local match is.
To make the recognition system less sensitive to the differences
between the facial expression displayed on the training and the
testing images, we weight the results obtained on each local area
on the bases of how much of this local area is affected by the
expression displayed on the current test image.