Dictionary Integration using 3D Morphable Face Models for Pose-invariant Collaborative-representation-based Classification
This addresses pose variations in face recognition for security or biometric applications, but it is incremental as it builds on existing collaborative-representation-based classification methods.
The paper tackled pose-invariant face classification by integrating 3D morphable models to augment training data with virtual poses and using an on-line elimination scheme to optimize the dictionary, resulting in demonstrated robustness to pose variations in experiments on face datasets.
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.