Face recognition using improved principal component analysis

Nara, Yoshiharu; Yang, Jianming; Suematsu, Yoshikazu

doi:10.1109/mhs.2003.1249913

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…Since the first applications of PCA [21], this technique has found its way into a wide range of different application areas, for example signal processing [75], factor analysis [29,44], system identification [77], chemometrics [20,66] and more recently, general data mining [11,70,58] including image processing [17,72] and pattern recognition [47,10], as well as process 2 U. Kruger, J. Zhang, and L. Xie monitoring and quality control [1,82] including multiway [48], multiblock [52] and multiscale [3] extensions. This success is mainly related to the ability of PCA to describe significant information/variation within the recorded data typically by the first few score variables, which simplifies data analysis tasks accordingly.…”

Section: Introductionmentioning

confidence: 99%

Developments and Applications of Nonlinear Principal Component Analysis – a Review

Krüger

Zhang

Xie

2008

Lecture Notes in Computational Science and Enginee

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Summary.Although linear principal component analysis (PCA) originates from the work of Sylvester [67] and Pearson [51], the development of nonlinear counterparts has only received attention from the 1980s. Work on nonlinear PCA, or NLPCA, can be divided into the utilization of autoassociative neural networks, principal curves and manifolds, kernel approaches or the combination of these approaches. This article reviews existing algorithmic work, shows how a given data set can be examined to determine whether a conceptually more demanding NLPCA model is required and lists developments of NLPCA algorithms. Finally, the paper outlines problem areas and challenges that require future work to mature the NLPCA research field. IntroductionPCA is a data analysis technique that relies on a simple transformation of recorded observation, stored in a vector z ∈ R N , to produce statistically independent score variables, stored in t ∈ R n , n ≤ N :(1.1)Here, P is a transformation matrix, constructed from orthonormal column vectors. Since the first applications of PCA [21], this technique has found its way into a wide range of different application areas, for example signal processing [75], factor analysis [29,44], system identification [77], chemometrics [20,66] and more recently, general data mining [11,70,58] including image processing [17,72] and pattern recognition [47,10], as well as process 2 U. Kruger, J. Zhang, and L. Xie monitoring and quality control [1,82] including multiway [48], multiblock [52] and multiscale [3] extensions. This success is mainly related to the ability of PCA to describe significant information/variation within the recorded data typically by the first few score variables, which simplifies data analysis tasks accordingly. Sylvester [67] formulated the idea behind PCA, in his work the removal of redundancy in bilinear quantics, that are polynomial expressions where the sum of the exponents are of an order greater than 2, and Pearson [51] laid the conceptual basis for PCA by defining lines and planes in a multivariable space that present the closest fit to a given set of points. Hotelling [28] then refined this formulation to that used today. Numerically, PCA is closely related to an eigenvector-eigenvalue decomposition of a data covariance, or correlation matrix and numerical algorithms to obtain this decomposition include the iterative NIPALS algorithm [78], which was defined similarly by Fisher and MacKenzie earlier on [80], and the singular value decomposition. Good overviews concerning PCA are given in Mardia et al. [45], Joliffe [32], Wold et al. [80] and Jackson [30].The aim of this article is to review and examine nonlinear extensions of PCA that have been proposed over the past two decades. This is an important research field, as the application of linear PCA to nonlinear data may be inadequate [49]. The first attempts to present nonlinear PCA extensions include a generalization, utilizing a nonmetric scaling, that produces a nonlinear optimization problem [42] and constructing a curves...

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Section: Introductionmentioning

confidence: 99%

Developments and Applications of Nonlinear Principal Component Analysis – a Review

Krüger

Zhang

Xie

2008

Lecture Notes in Computational Science and Enginee

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“…For the feature extraction, we use a well-known technique called principal component analysis (PCA), also known as Eigenface for face recognition [10][11][12]. The major objective of PCA is to project the high dimensional visual stimuli i.e.…”

Section: Face Feature Vector Generationmentioning

confidence: 99%

“…face images into a lower dimensional space. PCA is an optimal method for dimensionality reduction in the sense of mean-square error (MSE) [11,12].…”

Section: Face Feature Vector Generationmentioning

confidence: 99%

Privacy-preserving and tokenless chaotic revocable face authentication scheme

2010

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With the large-scale proliferation of biometric systems, privacy and irrevocability issues of their data have become a hot research issue. Recently, some researchers proposed a couple of schemes to generate BioHash, e.g. PalmHash, however, in this paper, we point out that the previous schemes are costly in terms of computational complexity and possession of USB tokens to generate pseudorandom numbers. To overcome the problems, we propose a novel chaotic FaceHashing scheme which preserves privacy of a biometric user. The presented scheme does not need users to posses USB tokens in generating pseudorandom sequences, which is a cost-effective solution. Besides, our scheme minimizes the system complexity with simple operations to attain the FaceHash. Experimental results show that the proposed scheme is efficient, secure, and revocable in case FaceHash is theft or compromised.

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“…The use of the log-polar transform (LPT) [1,2] in pattern recognition applications is not new, different research groups have employed it. Nara has used the LPT with improved principal component analysis (PCA) for the recognition of faces [1] , Tistarelli has implemented an active face recognition system that uses the LPT together with PCA [2] .…”

Section: Introductionmentioning

confidence: 99%

Scale and rotation invariant ship recognition using log-polar mapping and two-dimensional PCA

Zhao

Feng

2011

SPIE Proceedings

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A novel scale and rotation invariant ship recognition method using log-polar mapping and two-dimensional principal component analysis (2DPCA) is proposed. Log-polar mapping is very useful for eliminating the rotation and scale effects. 2DPCA is used to extract ship feature from normalized log-polar image become scale and rotation invariant. Experimental results show that the proposed method has improved recognition performance.

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Face recognition using improved principal component analysis

Cited by 6 publications

References 6 publications

Developments and Applications of Nonlinear Principal Component Analysis – a Review

Developments and Applications of Nonlinear Principal Component Analysis – a Review

Privacy-preserving and tokenless chaotic revocable face authentication scheme

Scale and rotation invariant ship recognition using log-polar mapping and two-dimensional PCA

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