人脸识别论文
4星 · 超过85%的资源 需积分: 0 102 浏览量
2008-03-25
14:58:13
上传
评论 4
收藏 2.31MB PDF 举报 
Eigenfeature Regularization and Extraction
in Face Recognition
Xudong Jiang, Senior Member, IEEE, Bappaditya Mandal, and Alex Kot, Fellow, IEEE
Abstract—This work proposes a subspace approach that regularizes and extracts eigenfeatures from the face image. Eigenspace of the
within-class scatter matrix is decomposed into three subspaces: a reliable subspace spanned mainly by the facial variation, an unstable
subspace due to noise and finite number of training samples, and a null subspace. Eigenfeatures are regularized differently in these three
subspaces based on an eigenspectrum model to alleviate problems of instability, overfitting, or poor generalization. This also enables the
discriminant evaluation performed in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after
the discriminant assessment. These efforts facilitate a discriminative and a stable low-dimensional feature representation of the face
image. Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR, and GT
databases show that our method consistently outperforms others.
Index Terms—Face recognition, linear discriminant analysis, regularization, feature extraction, subspace methods.
Ç
1INTRODUCTION
F
ACE recognition has attracted many researchers in the area
of pattern recognition, machine learning, and computer
vision because of its immense application potential. Numer-
ous methods have been proposed in the last two decades [1],
[2]. However, there are still substantial challenging problems,
which remain to be unsolved. One of the critical issues is how
to extract discriminative and stable features for classification.
Linear subspace analysis has been extensively studied and
becomes a popular feature extraction method since the
principal component analysis (PCA) [3], Bayesian maximum
likelihood (BML) [4], [5], [6], and linear discriminant analysis
(LDA) [7], [8] were introduced into face recognition. A
theoretical analysis showed that a low-dimensional linear
subspace could capture the set of images of an object
produced by a variety of lighting conditions [9]. The agree-
able properties of the linear subspace analysis and its
promising performance achieved in the face recognition
encourage researchers to extend it to higher order statistics
[10], [11], nonlinear methods [12], [13], [14], Gabor features
[15], [16], and localit y preserving projections [17], [18].
However, the basic linear subspace analysis has still out-
standing challenging problems when applied to the face
recognition due to the high dimensionality of face images and
the finite number of training samples in practice.
PCA maximizes the variances of the extracted features
and, hence, minimizes the reconstruction error and removes
noise residing in the discarded dimensi ons. The best
representation of data may not perform well from the
classification point of view because the total scatter matrix
is contributed by both the within and between-class varia-
tions. To differentiate face images of one person from those of
the others, the discrimination of the features is the most
important. LDA is an efficient way to extract the discrimina-
tive features as it handles the within and between-class
variations separately. However, this method needs the
inverse of the within-class scatter matrix. This is problematic
in many practical face recognition tasks because the dimen-
sionality of the face image is usually very high compared to
the number of available training samples and, hence, the
within-class scatter matrix is often singular.
Numerous methods have been proposed to solve this
problem in the last decade. A popular approach called
Fisherface (FLDA) [19] applies PCA first for dimensionality
reduction so as to make the within-class scatter matrix
nonsingular before the application of LDA. However,
applying PCA for dimensionality reduction may result in
the loss of discriminative information [20], [21], [22]. Direct
LDA (DLDA) method [23], [24] removes null space of the
between-class scatter matrix and extracts the eigenvectors
corresponding to the smallest eigenvalues of the within-class
scatter matrix. It is an open question of how to scale the
extracted features, as the smallest eigenvalues are very
sensitive to noise. The null space (NLDA) approaches [25],
[26], [22] assume that the null space contains the most
discriminative information. Interestingly, this appears to be
contradicting the popular FLDA that only uses the principal
space and discards the null space. A common problem of all
these approaches is that they all lose some discriminative
information, either in the principal or in the null space.
In fact, the discriminative information resides in both
subspaces. To use both subspaces, a modified LDA approach
[27] replaces the within-class scatter matrix by the total scatter
matrix. Subsequent work [28] extracts features separately
from the principal and null spaces of the within-class scatter
matrix. However, the extracted features may not be properly
scaled andundueemphasis isplaced on the null space in these
two approaches due to the replacement of the within-class
scatter matrix by the total scatter matrix. The dual-space
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 30, NO. 3, MARCH 2008 383
. The authors are with the School of Electrical and Electronic Engineering,
Nanyang Technological University, Nanyang Link, Singapore 639798.
E-mail: {exdjiang, bapp0001, eackot}@ntu.edu.sg.
Manuscript received 27 June 2006; revised 6 Nov. 2006; accepted 15 May
2007; published online 31 May 2007.
Recommended for acceptance by T. Tan.
For information on obtaining reprints of this article, please send e-mail to:
tpami@computer.org, and reference IEEECS Log Number TPAMI-0475-0606.
Digital Object Identifier no. 10.1109/TPAMI.2007.70708.
0162-8828/08/$25.00 ß 2008 IEEE Published by the IEEE Computer Society
剩余11页未读，继续阅读
评论9