Unsupervised joint alignment of complex images

Many recognition algorithms depend on careful posi-tioning of an object into a canonical pose, so the position of features relative to a xed coordinate system can be ex-amined. Currently, this positioning is done either manu-ally or by training a class-specialized learning algorithm with samples of the class that have been hand-labeled with parts or poses. In this paper, we describe a novel method to achieve this positioning using poorly aligned examples of a class with no additional labeling. Given a set of unaligned examplars of a class, such as faces, we automatically build an alignment mechanism, without any additional labeling of parts or poses in the data set. Using this alignment mecha-nism, new members of the class, such as faces resulting from a face detector, can be precisely aligned for the recognition process. Our alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm specically developed for and trained on hand-labeled face images. We also demonstrate its use on an entirely different class of ob-jects (cars), again without providing any information about parts or pose to the learning algorithm. 1