Details

Autor(en) / Beteiligte

• Si Si
• Dacheng Tao
• Kwok-Ping Chan

Titel

Discriminative Hessian Eigenmaps for face recognition

Ist Teil von

• 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, p.5586-5589

Ort / Verlag

IEEE

Erscheinungsjahr

2010

Quelle

IEL

Beschreibungen/Notizen

• Dimension reduction algorithms have attracted a lot of attentions in face recognition because they can select a subset of effective and efficient discriminative features in the face images. Most of dimension reduction algorithms can not well model both the intra-class geometry and interclass discrimination simultaneously. In this paper, we introduce the Discriminative Hessian Eigenmaps (DHE), a novel dimension reduction algorithm to address this problem. DHE will consider encoding the geometric and discriminative information in a local patch by improved Hessian Eigenmaps and margin maximization respectively. Empirical studies on public face database thoroughly demonstrate that DHE is superior to popular algorithms for dimension reduction, e.g., FLDA, LPP, MFA and DLA.