Face Identification Using Large Feature Sets

Autor(es)

Schwartz, William Robson ^[1] ; Guo, Huimin ^[2] ; Choi, Jonghyun ^[3] ; Davis, Larry S. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil

^[2] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 - USA

^[3] Univ Maryland, Dept Elect & Comp Engn, College Pk, MD 20742 - USA

• Tipo de documento: Artigo Científico
• Fonte: IEEE Transactions on Image Processing; v. 21, n. 4, p. 2245-2255, APR 2012.
• Citações Web of Science: 52

Resumo

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. (AU)

• Processo FAPESP: 10/10618-3 - Combinação de Descritores de Características para Análise de Vídeos Contendo Humanos
• Beneficiário: William Robson Schwartz
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado