Robust and Efficient Parametric Face Alignment

We propose a correlation-based approach to parametric object alignment particularly suitable for face analysis applications which require efficiency and robustness against occlusions and illumination changes. Our algorithm registers two images by iteratively maximizing their correlation coefficient using gradient ascent. We compute this correlation coefficient from complex gradients which capture the orientation of image structures rather than pixel intensities. The maximization of this gradient correlation coefficient results in an algorithm which is as computationally efficient as ℓ2 norm-based algorithms, can be extended within the inverse compositional framework (without the need for Hessian re-computation) and is robust to outliers. To the best of our knowledge, no other algorithm has been proposed so far having all three features. We show the robustness of our algorithm for the problem of face alignment in the presence of occlusions and non-uniform illumination changes. The code that reproduces the results of our paper can be found at http://ibug.doc.ic.ac.uk/resources.