Introduction - If you have any usage issues, please Google them yourself
Many recognition algorithms depend on careful positioning of an object into a canonical pose, so the position of features relative to a fixed coordinate system can be examined. Generally, this positioning is done either manually or by training a class-specialized learning algorithm with samples of the class that have been hand-labeled with parts or poses. Due to the amount of supervision required by these methods, the alignment step of the recognition pipeline (below) is often ignored, under the assumption that the initial detection will perform rough alignment. In this project, we explore methods for aligning face images using low levels of supervision, for instance only using poorly aligned face images given as the output of a Viola-Jones face detector. We measure performance by comparing the recognition results using the images from detection compared with the aligned faces.