NCJ Number
305989
Journal
Advances in Biometrics Volume: 4642 Dated: 2007 Pages: 838-847
Date Published
2007
Length
10 pages
Annotation
This paper presents a horizontal and vertical 2D principal component analysis (2DPCA) based discriminant analysis (HVDA) method for face verification.
Abstract
The HVDA method, which derives features by applying DPCA horizontally and vertically on the image matrices (2D arrays), achieves high computational efficiency compared with the traditional PCA and/or LDA based methods that operate on high dimensional image vectors (1D arrays). The HVDA method further performs discriminant analysis to enhance the discriminating power of the horizontal and vertical 2DPCA features. Finally, the HVDA method takes advantage of the color 305990 What kind of color spaces is suitable for color face recognition?
Date Published: January 1, 2007
Downloads
Similar Publications
- A Feature Mapping Technique for Complex Data Object Generation With Likelihood and Deep Generative Approaches
- Understanding the Potential for Multidisciplinary Threat Assessment and Management Teams to Prevent Terrorism: Conducting a Formative Evaluation of the MassBay Threat Assessment Team
- Learning about ShotSpotter — and Gun Violence — from Chicago