  The paper presents a dictionary integration algorithm using 3D morphable face
models (3DMM) for pose-invariant collaborative-representation-based face
classification. To this end, we first fit a 3DMM to the 2D face images of a
dictionary to reconstruct the 3D shape and texture of each image. The 3D faces
are used to render a number of virtual 2D face images with arbitrary pose
variations to augment the training data, by merging the original and rendered
virtual samples to create an extended dictionary. Second, to reduce the
information redundancy of the extended dictionary and improve the sparsity of
reconstruction coefficient vectors using collaborative-representation-based
classification (CRC), we exploit an on-line elimination scheme to optimise the
extended dictionary by identifying the most representative training samples for
a given query. The final goal is to perform pose-invariant face classification
using the proposed dictionary integration method and the on-line pruning
strategy under the CRC framework. Experimental results obtained for a set of
well-known face datasets demonstrate the merits of the proposed method,
especially its robustness to pose variations.
