In this paper, we present an effective technique on discriminative feature extraction for face recognition. The proposed technique incorporates Graph Embedding and the Fisher's criterion where we call it as Neighbourhood Preserving Discriminant Embedding (NPDE). Utilizing the Graph Embedding criterion, the underlying nonlinear face data structure is revealed as representative and discriminative features for analysis. We employ Neighbourhood Preserving Embedding (NPE) for the purpose. NPE takes into account the restriction that neighbouring points in the high-dimensional space must remain within the same neighbourhood in the low dimension space and be located in a similar relative spatial situation (without changing the local structure of the nearest neighbours of each data point). Furthermore, by taking the advantage of the Fisher's criterion, the discriminating power of NPDE is further boosted. Based on this intuition, NPDE obtains better discriminative capability and experimentally verified in ORL, PIE and FRGC.
|Number of pages||11|
|Journal||Journal of Visual Communication and Image Representation|
|Publication status||Published - 2009 Nov|
Bibliographical noteFunding Information:
This work was supported by the Korea Science and Engineering Foundation (KOSET) through the Biometrics Engineering Research Center (BERC) at Yonsei University (Grant No. R112002105080020 (2009)).
All Science Journal Classification (ASJC) codes
- Signal Processing
- Media Technology
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering