Newton optimization based Congealing for facial image alignment

Congealing is an unsupervised image alignment method for a set of images, and the transformation parameters are obtained by minimizing a sum-of-entropies function. In this paper, we provide a solution to improve the estimation of transformation parameters using a Newton optimization method, under the premise of maintaining the compatibility with feature descriptors. Besides, instead of SIFT descriptor in canonical Congealing, we combine Congealing with POEM (Patterns of Oriented Edge Magnitudes) which catches both edge in- formation and the relation between pixels at a neighboring region. The experiment results show that our alignment method has better ability for the removal of unwanted displacements, and also improves the performance of face recognition.