Face Identification Using Large Feature Sets

TitleFace Identification Using Large Feature Sets
Publication TypeJournal Articles
Year of Publication2012
AuthorsSchwartz WR, Guo H, Choi J, Davis LS
JournalImage Processing, IEEE Transactions on
Pagination2245 - 2255
Date Published2012/04//
ISBN Number1057-7149
Keywords(mathematics);, approximations;trees, challenge, data, data;tree-based, discriminative, environments;face, feature, FERET;FRGC, grand, identification, least, recognition, recognition;least, sets;face, sets;facial, squares, squares;training, structure;uncontrolled, task;face, technology;multichannel, weighting;partial

With the goal of matching unknown faces against a gallery of known people, the face identification task has been studied for several decades. There are very accurate techniques to perform face identification in controlled environments, particularly when large numbers of samples are available for each face. However, face identification under uncontrolled environments or with a lack of training data is still an unsolved problem. We employ a large and rich set of feature descriptors (with more than 70 000 descriptors) for face identification using partial least squares to perform multichannel feature weighting. Then, we extend the method to a tree-based discriminative structure to reduce the time required to evaluate probe samples. The method is evaluated on Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. Experiments show that our identification method outperforms current state-of-the-art results, particularly for identifying faces acquired across varying conditions.