Multilinear principal component analysis for face recognition with fewer features

Wang, Jin; Armando, Barreto; Wang, Lu; Chen, Yu; Rishe, Naphtali; Andrian, Jean; Adjouadi, Malek

doi:10.1016/j.neucom.2009.08.022

Cited by 36 publications

(18 citation statements)

References 22 publications

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“…In the MPCA method [24], [25] 17) It is noted that the features obtained from the two methods are different; in that the features obtained from the two methods are different; in that, the first one provides K(I) x 12 + K(2) X II features.…”

Section: B Multilinear Principal Component Analysis (Mpca)mentioning

confidence: 99%

Performance evaluation of image compression on PCA-based face recognition systems

John

Williams

2012

2012 12th International Conference on Hybrid Intelligent Systems (HIS)

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Image compression has received tremendous research input in recent times; however, only few works have been done with respect to face recognition regardless of the fact that, usually, face images need to be transmitted across networks for recognition purposes as seen in surveillance systems. This work analytically investigates the effects of image compression on recognition accuracies of three selected PCA based algorithms. The algorithms investigated include standard PCA, MPCA and MPCALDA algorithms. As a form of an extensive research, experiments conducted include both in compressed and uncompressed domains where the three algorithms have been exhaustively analyzed. We statistically present the results obtained which shows no significant depreciation in the recognition accuracies.

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Section: B Multilinear Principal Component Analysis (Mpca)mentioning

confidence: 99%

Performance evaluation of image compression on PCA-based face recognition systems

John

Williams

2012

2012 12th International Conference on Hybrid Intelligent Systems (HIS)

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“…PCA is a statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (PCs) [2][3][4]. PCA is commonly used for dimension reduction [5,6] by specifying a few PCs that account for as much of the variability in the original dataset as possible. It is well known that PCA has been primarily developed for single-valued variables.…”

Section: Introductionmentioning

confidence: 99%

Sampling Based Histogram PCA and Its Mapreduce Parallel Implementation on Multicore

Wang

et al. 2018

Symmetry

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Abstract:In existing principle component analysis (PCA) methods for histogram-valued symbolic data, projection results are approximated based on Moore's algebra and fail to reflect the data's true structure, mainly because there is no precise, unified calculation method for the linear combination of histogram data. In this paper, we propose a new PCA method for histogram data that distinguishes itself from various well-established methods in that it can project observations onto the space spanned by principal components more accurately and rapidly by sampling through a MapReduce framework. The new histogram PCA method is implemented under the same assumption of "orthogonal dimensions for every observation" with the existing literatures. To project observations, the method first samples from the original histogram variables to acquire single-valued data, on which linear combination operations can be performed. Then, the projection of observations can be given by linear combination of loading vectors and single-valued samples, which is close to accurate projection results. Finally, the projection is summarized to histogram data. These procedures involve complex algorithms and large-scale data, which makes the new method time-consuming. To speed it up, we undertake a parallel implementation of the new method in a multicore MapReduce framework. A simulation study and an empirical study confirm that the new method is effective and time-saving.

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“…A promising way to deal with this issue is to use multidimensional array-based representation [29,27]. Actually, this type of representation has been successfully applied to EEG signals classification in biomedical engineering [26,25,7], image processing in computer vision or pattern recognition [47,44,21], and other fields [40,33,12].…”

Section: Introductionmentioning

confidence: 99%

A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli

Sun

Zhang

Peng

2017

Artificial Intelligence in Medicine

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Multilinear principal component analysis for face recognition with fewer features

Cited by 36 publications

References 22 publications

Performance evaluation of image compression on PCA-based face recognition systems

Performance evaluation of image compression on PCA-based face recognition systems

Sampling Based Histogram PCA and Its Mapreduce Parallel Implementation on Multicore

A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli

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