Face identification using large feature sets

William Robson Schwartz, Huimin Guo, Jonghyun Choi, Larry S. Davis

Research output: Contribution to journalArticlepeer-review

70 Citations (Scopus)

Abstract

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 70000 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.

Original languageEnglish
Article number6086621
Pages (from-to)2245-2255
Number of pages11
JournalIEEE Transactions on Image Processing
Volume21
Issue number4
DOIs
Publication statusPublished - 2012 Apr

Bibliographical note

Funding Information:
Manuscript received February 21, 2011; revised June 22, 2011; accepted October 29, 2011. Date of publication November 22, 2011; date of current version March 21, 2012. This work was supported in part by Intelligence Advanced Research Projects Activity funded by the Office of the Director of National Intelligence through the Army Research Laboratory and in part by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) under Grant 2010/10618-3. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Alex ChiChung Kot.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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