Existing face recognition
algorithms generally rely on local and global facial features and
any variation can affect the recognition performance. This paper
introduces plastic surgery as a new dimension for face recognition
algorithms. We present an experimental study to quantitatively evaluate
the performance of face recognition algorithms on a plastic
surgery database that contains face images with both local and global
surgeries. The study shows that appearance, feature, and texture based
algorithms are unable to effectively mitigate the variations caused
by the plastic surgery procedures.