Horizontal and Vertical 2DPCA-Based Discriminant Analysis for Face Verification on a Large-Scale Database

Authors:
Jian Yang;Chengjun Liu
Affiliations:
New Jersey Inst. of Technol., Newark;-
Venue:
IEEE Transactions on Information Forensics and Security
Year:
2007

Citing 0
Cited 10

Face recognition using new image representations

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Color space normalization: Enhancing the discriminating power of color spaces for face recognition

Pattern Recognition
Emulating biological strategies for uncontrolled face recognition

Pattern Recognition
What kind of color spaces is suitable for color face recognition?

Neurocomputing
Face recognition using second-order discriminant tensor subspace analysis

Neurocomputing
Iris recognition based on robust iris segmentation and image enhancement

International Journal of Biometrics
Block principal component analysis with L1-norm for image analysis

Pattern Recognition Letters
Active shape model with inter-profile modeling paradigm for cardiac right ventricle segmentation

MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fusing magnitude and phase features for robust face recognition

ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Feature local binary patterns with application to eye detection

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper first discusses some theoretical properties of 2D principal component analysis (2DPCA) and then presents a horizontal and vertical 2DPCA-based discriminant analysis (HVDA) method for face verification. The HVDA method, which applies 2DPCA horizontally and vertically on the image matrices (2D arrays), achieves lower computational complexity than the traditional PCA and Fisher linear discriminant analysis (LDA)-based methods that operate on high dimensional image vectors (1D arrays). The horizontal 2DPCA is invariant to vertical image translations and vertical mirror imaging, and the vertical 2DPCA is invariant to horizontal image translations and horizontal mirror imaging. The HVDA method is therefore less sensitive to imprecise eye detection and face cropping, and can improve upon the traditional discriminant analysis methods for face verification. Experiments using the face recognition grand challenge (FRGC) and the biometric experimentation environment system show the effectiveness of the proposed method. In particular, for the most challenging FRGC version 2 Experiment 4, which contains 12thinspace776 training images, 16 028 controlled target images, and 8014 uncontrolled query images, the HVDA method using a color configuration across two color spaces, namely, the YIQ and the YCbCr color spaces, achieves the face verification rate (ROC III) of 78.24% at the false accept rate of 0.1%.