  To address the sequential changes of images including poses, in this paper we
propose a recurrent regression neural network(RRNN) framework to unify two
classic tasks of cross-pose face recognition on still images and video-based
face recognition. To imitate the changes of images, we explicitly construct the
potential dependencies of sequential images so as to regularize the final
learning model. By performing progressive transforms for sequentially adjacent
images, RRNN can adaptively memorize and forget the information that benefits
for the final classification. For face recognition of still images, given any
one image with any one pose, we recurrently predict the images with its
sequential poses to expect to capture some useful information of others poses.
For video-based face recognition, the recurrent regression takes one entire
sequence rather than one image as its input. We verify RRNN in static face
dataset MultiPIE and face video dataset YouTube Celebrities(YTC). The
comprehensive experimental results demonstrate the effectiveness of the
proposed RRNN method.
