04-10-2014, 11:59 AM
Curvelet Based Feature Extraction
[attachment=68823]
. Introduction
Designing a completely automatic and efficient face recognition system is a grand challenge
for biometrics, computer vision and pattern recognition researchers. Generally, such a
recognition system is able to perform three subtasks: face detection, feature extraction and
classification. We’ll put our focus on feature extraction, the crucial step prior to
classification. The key issue here is to construct a representative feature set that can enhance
system-performance both in terms of accuracy and speed.
At the core of machine recognition of human faces is the extraction of proper features. Direct
use of pixel values as features is not possible due to huge dimensionality of the faces.
Traditionally, Principal Component Analysis (PCA) is employed to obtain a lower
dimensional representation of the data in the standard eigenface based methods [Turk and
Pentland 1991]. Though this approach is useful, it suffers from high computational load and
fails to well-reflect the correlation of facial features. The modern trend is to perform
multiresolution analysis of images. This way, several problems like, deformation of images
due to in-plane rotation, illumination variation and expression changes can be handled with
less difficulty.
Multiresolution ideas have been widely used in the field of face recognition. The most
popular multiresolution analysis tool is the Wavelet Transform. In wavelet analysis an
image is usually decomposed at different scales and orientations using a wavelet basis
vector. Thereafter, the component corresponding to maximum variance is subjected to
‘further operation’. Often this ‘further operation’ includes some dimension reduction before
feeding the coefficients to classifiers like Support Vector Machine (SVM), Neural Network
(NN) and Nearest Neighbor. This way, a compact representation of the facial images can be
achieved and the effect of variable facial appearances on the classification systems can also
be reduced. The wide-spread popularity of wavelets has stirred researchers’ interest in
multiresolution and harmonic analysis. Following the success of wavelets, a series of
multiresolution, multidimensional tools, namely contourlet, curvelet, ridgelet have been
developed in the past few years. In this chapter, we’ll concentrate on Digital Curvelet
Transform. First, the theory of curvelet transform will be discussed in brief. Then we'll talk
about the potential of curvelets as a feature descriptor, looking particularly into the problem
of image-based face recognition. Some experimental results from recent scientific works will
be provided for ready reference
Applications
Curvelet transform is gaining popularity in different research areas, like signal processing,
image analysis, seismic imaging since the development of FDCT in 2006. It has been
successfully applied in image denoising [Starck et al. 2002], image compression, image
fusion [Choi et al., 2004], contrast enhancement [Starck et al., 2003], image deconvolution
[Starck et al., 2003], high quality image restoration [Starck et al., 2003], astronomical image
representation [Starck et al., 2002] etc. Examples of two applications, contrast enhancement
and denoising are presented in figures 5 and 6. Readers are suggested to go through the
referred works for further information on various applications of the curvelet transform.
Recently, curvelets have also been employed to address several pattern recognition
problems, such as face recognition [Mandal et al., 2007; Zhang et al., 2007] (discussed in
detail in section 3), optical character recognition [Majumdar, 2007], finger-vein pattern
recognition [Zhang et al., 2006] and palmprint recognition [Dong et al. 2005].
Conclusion
In this chapter, newly developed curvelet transform has been presented as a new tool for
feature extraction from facial images. Various algorithms are discussed along with relevant
experimental results as reported in some recent works on face recognition. Looking at the
results presented in tables 1, 2 and 3, we can infer that curvelet is not only a successful
feature descriptor, but is superior to many existing wavelet-based techniques. Results for
only one standard database (ORL) are listed here; nevertheless, work has been done on
other standard databases like, FERET, YALE, Essex Grimace, Georgia-Tech and Japanese
facial expression datasets. From the results presented in all these datasets prove the
superiority of curvelets over wavelets for the application of face recognition. Curvelet
features thus extracted from faces are also found to be robust against noise, significant
amount of illumination variation, facial details variation and extreme expression changes.
The works on face recognition using curvelet transform that exist in literature are not yet
complete and do not fully understand the capability of curvelet transform for face
recognition; hence, there is much scope of improvement in terms of both recognition
accuracy and curvelet-based methodology.
[attachment=68823]
. Introduction
Designing a completely automatic and efficient face recognition system is a grand challenge
for biometrics, computer vision and pattern recognition researchers. Generally, such a
recognition system is able to perform three subtasks: face detection, feature extraction and
classification. We’ll put our focus on feature extraction, the crucial step prior to
classification. The key issue here is to construct a representative feature set that can enhance
system-performance both in terms of accuracy and speed.
At the core of machine recognition of human faces is the extraction of proper features. Direct
use of pixel values as features is not possible due to huge dimensionality of the faces.
Traditionally, Principal Component Analysis (PCA) is employed to obtain a lower
dimensional representation of the data in the standard eigenface based methods [Turk and
Pentland 1991]. Though this approach is useful, it suffers from high computational load and
fails to well-reflect the correlation of facial features. The modern trend is to perform
multiresolution analysis of images. This way, several problems like, deformation of images
due to in-plane rotation, illumination variation and expression changes can be handled with
less difficulty.
Multiresolution ideas have been widely used in the field of face recognition. The most
popular multiresolution analysis tool is the Wavelet Transform. In wavelet analysis an
image is usually decomposed at different scales and orientations using a wavelet basis
vector. Thereafter, the component corresponding to maximum variance is subjected to
‘further operation’. Often this ‘further operation’ includes some dimension reduction before
feeding the coefficients to classifiers like Support Vector Machine (SVM), Neural Network
(NN) and Nearest Neighbor. This way, a compact representation of the facial images can be
achieved and the effect of variable facial appearances on the classification systems can also
be reduced. The wide-spread popularity of wavelets has stirred researchers’ interest in
multiresolution and harmonic analysis. Following the success of wavelets, a series of
multiresolution, multidimensional tools, namely contourlet, curvelet, ridgelet have been
developed in the past few years. In this chapter, we’ll concentrate on Digital Curvelet
Transform. First, the theory of curvelet transform will be discussed in brief. Then we'll talk
about the potential of curvelets as a feature descriptor, looking particularly into the problem
of image-based face recognition. Some experimental results from recent scientific works will
be provided for ready reference
Applications
Curvelet transform is gaining popularity in different research areas, like signal processing,
image analysis, seismic imaging since the development of FDCT in 2006. It has been
successfully applied in image denoising [Starck et al. 2002], image compression, image
fusion [Choi et al., 2004], contrast enhancement [Starck et al., 2003], image deconvolution
[Starck et al., 2003], high quality image restoration [Starck et al., 2003], astronomical image
representation [Starck et al., 2002] etc. Examples of two applications, contrast enhancement
and denoising are presented in figures 5 and 6. Readers are suggested to go through the
referred works for further information on various applications of the curvelet transform.
Recently, curvelets have also been employed to address several pattern recognition
problems, such as face recognition [Mandal et al., 2007; Zhang et al., 2007] (discussed in
detail in section 3), optical character recognition [Majumdar, 2007], finger-vein pattern
recognition [Zhang et al., 2006] and palmprint recognition [Dong et al. 2005].
Conclusion
In this chapter, newly developed curvelet transform has been presented as a new tool for
feature extraction from facial images. Various algorithms are discussed along with relevant
experimental results as reported in some recent works on face recognition. Looking at the
results presented in tables 1, 2 and 3, we can infer that curvelet is not only a successful
feature descriptor, but is superior to many existing wavelet-based techniques. Results for
only one standard database (ORL) are listed here; nevertheless, work has been done on
other standard databases like, FERET, YALE, Essex Grimace, Georgia-Tech and Japanese
facial expression datasets. From the results presented in all these datasets prove the
superiority of curvelets over wavelets for the application of face recognition. Curvelet
features thus extracted from faces are also found to be robust against noise, significant
amount of illumination variation, facial details variation and extreme expression changes.
The works on face recognition using curvelet transform that exist in literature are not yet
complete and do not fully understand the capability of curvelet transform for face
recognition; hence, there is much scope of improvement in terms of both recognition
accuracy and curvelet-based methodology.