The GIST of aligning faces
We propose a novel supervised initialization scheme for cascaded face alignment by searching nearest neighbors based on global image descriptors. Unlike existing schemes which resort to additional large training data sets for learning features, our method does not require additional training steps; thus making our method low computational. Moreover, we found that it is sufficient to use a simple low-dimensional global image descriptor that is easy to extract. In particular, in this work we use the GIST features as our global image descriptor. The proposed initialization scheme outperforms existing initialization schemes for face alignment and improves on the state-of-the-art methods on two challenging datasets, 300-W and COFW.