Sparse Tensor Discriminant Color Space for Face Verification

As one of the fundamental features, color provides useful information and plays an important role for face recognition. Generally, the choice of a color space is different for different visual tasks. How can a color space be sought for the specific face recognition problem? To address this problem,...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2012-06, Vol.23 (6), p.876-888
Main Authors: WANG, Su-Jing, JIAN YANG, SUN, Ming-Fang, PENG, Xu-Jun, SUN, Ming-Ming, ZHOU, Chun-Guang
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Biometry - methods
Color
Color images
Colorimetry - methods
Computer science
control theory
systems
Connectionism. Neural networks
Detection, estimation, filtering, equalization, prediction
discriminant information
Exact sciences and technology
Face
Face - anatomy & histology
Face recognition
Feature extraction
Humans
Image color analysis
Image Interpretation, Computer-Assisted - methods
Information, signal and communications theory
Mathematical analysis
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Regression
Reproducibility of Results
Robustness
Sensitivity and Specificity
Signal and communications theory
Signal, noise
Sparse matrices
sparse representation
Studies
Telecommunications and information theory
Tensile stress
tensor subspace
Tensors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As one of the fundamental features, color provides useful information and plays an important role for face recognition. Generally, the choice of a color space is different for different visual tasks. How can a color space be sought for the specific face recognition problem? To address this problem, we propose a sparse tensor discriminant color space (STDCS) model that represents a color image as a third-order tensor in this paper. The model cannot only keep the underlying spatial structure of color images but also enhance robustness and give intuitionistic or semantic interpretation. STDCS transforms the eigenvalue problem to a series of regression problems. Then one spare color space transformation matrix and two sparse discriminant projection matrices are obtained by applying lasso or elastic net on the regression problems. The experiments on three color face databases, AR, Georgia Tech, and Labeled Faces in the Wild face databases, show that both the performance and the robustness of the proposed method outperform those of the state-of-the-art TDCS model.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2012.2191620