PFP-PCA: Parallel Fixed Point PCA Face Recognition

Rujirakul, Kanokmon; So-In, Chakchai; Arnonkijpanich, Banchar; Sunat, Khamron; Poolsanguan, Sarayut

doi:10.1109/isms.2013.38

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Although there are several preprocessing techniques, briefly stated in related work section, here, a necessary parallel method is considered to aid algorithm efficiency, that is, gray-scale conversion. Regarding our previous experiment, the recognition precision between color and gray-scale images is not significantly different but with the increase of computational time complexity [ 27 ]. Thus, in our proposed architecture, our focus is to parallelly perform gray-scale conversions once the input images are shaped into the same size, that is, 180 × 200 pixels in both training and testing processes as shown in Figure 4 .…”

Section: Parallel Expectation-maximization Pca Face Recognition Armentioning

confidence: 99%

PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

Rujirakul

So-In

Arnonkijpanich

2014

The Scientific World Journal

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Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

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Section: Parallel Expectation-maximization Pca Face Recognition Armentioning

confidence: 99%

PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

Rujirakul

So-In

Arnonkijpanich

2014

The Scientific World Journal

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“…Principal component analysis (PCA) is such an analysis method that can effectively reduce the dimension of parameters and retain the effective influence components in all cost influencing parameters. At present, the PCA method plays an important role in image compression [17], face recognition [18], [19], and image representation [20], [21]. However, it has not received much attention and application in solving the problems of multi-collinearity of data and improving the prediction accuracy of model.…”

Section: Introductionmentioning

confidence: 99%

Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

Chen

Huang

2020

IEEE Access

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The major objective of this paper is to build a cost prediction model for general aviation aircraft using artificial neural network (ANN) and principle component analysis (PCA) methods. A total number of 22 samples of general aviation aircraft collected from the literature are utilized to train and test the model. In the PCA, eigenvalues of PC1 and PC2 are 6.987 and 1.529, respectively, indicating that they have the strongest interpretation of the original variable information and are retained as cost influencing variables to train the ANN model. The pure multiple linear regression (MLR), stepwise regression (SR) and ANN models are built respectively for comparison. The comparative results reveal that the ANN method has better estimation effect than MLR and SR models in case of multi-collinearity of data. Combined with PCA, the ANN model is optimized, with MAPE, MAE, R and RMSE values of training and testing samples to be 0.009 and 0.015, 1.222 and 3, 0.9999 and 0.9994, 1.667 and 3.416, respectively. Finally, a more accurate and practical prediction model is developed. More importantly, this research can provide an important reference for general aviation aircraft companies in term of product cost planning and corporate sales strategy.

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“…A dimensionality reduction procedure reduces the number of elements in each data point, shrinking the data from the perspective of dimensionality without touching the numerical representation; a quantisation procedure reduces the precision of data elements, cutting down the data size from the perspective of numeric precision without affecting the dimensionality. A straightforward workflow to prepare LDLP data is to apply dimensionality reduction followed by quantisation [1]. However, this workflow can incur information loss and reduce the accuracy of the downstream data analysis system on the FPGA platform.…”

Section: Introductionmentioning

confidence: 99%

Quantisation-aware Dimensionality Reduction

Guo

Luk

2020

2020 International Conference on Field-Programmable Technology (ICFPT)

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Typical data analysis systems involving FPGAs work better with low-dimensional low-precision (LDLP) data than with high-dimensional high-precision (HDHP) ones due to limitations on data bandwidth and on-chip resources. However, data sources usually offer HDHP data matrices. It is possible to obtain LDLP data that approximate HDHP data by applying dimensionality reduction and quantisation. However, this straightforward workflow incurs significant information loss that reduces the accuracy of data analysis on FPGAs. This paper proposes that the information loss in the straightforward workflow is due to the dimensionality reduction algorithm's unawareness of quantisation. To address this problem, the paper introduces Quadir, a novel algorithm that supports quantisation-aware dimensionality reduction. In particular, Quadir is an alternating direction optimisation algorithm that finds a transformation to project an HDHP data matrix to an LDHP one so that the LDLP matrix after quantisation can reconstruct the original HDHP matrix with optimised approximation. Our experimental evaluation in data reconstruction shows that the LDHP data matrices produced by Quadir preserve more information from the original data than principal component analysis (PCA) after quantisation.

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PFP-PCA: Parallel Fixed Point PCA Face Recognition

Cited by 9 publications

References 16 publications

PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

Quantisation-aware Dimensionality Reduction

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