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An Individual Local Mean-based 2DPCA for Face Recognition under Illumination Effects | IEEE Conference Publication | IEEE Xplore

An Individual Local Mean-based 2DPCA for Face Recognition under Illumination Effects


Abstract:

Principal component analysis (PCA) is a classical technique in pattern recognition and computer vision. It is one of the most successful techniques for face recognition. ...Show More

Abstract:

Principal component analysis (PCA) is a classical technique in pattern recognition and computer vision. It is one of the most successful techniques for face recognition. The PCA consists of two main steps including (I) covariance matrix calculation and (II) eigenvector and eigenvalue extraction. In case of face recognition, the input image is converted to the vector form before forwarding to covariance matrix computation. Then, the matrix is used to extract the eigenvector and eigenvalue. Two-dimensional PCA (2DPCA) is introduced to reduce high-dimensional problems. The illumination effect problems in the face recognition is still needed to be resolved. In order to improve and solve the problems, this paper proposes an individual local mean-based 2DPCA (ILM-2DPCA), which replaces a single local mean in 2DPCA method. The individual local mean can provide more appropriate mean to each image, which can reduce the illumination effect effectively. The experimental study is set up on dataset Yale face database B+. The results indicate that the proposed method outperforms, based on the accuracy rate, all the baseline methods which are 2DPCA, I-2DPCA, Bi2DPCA and 2D2PCA.
Date of Conference: 10-12 July 2019
Date Added to IEEE Xplore: 14 October 2019
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Conference Location: Chonburi, Thailand
Faculty of Science, Naresuan University, Phitsanulok, THAILAND
Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, THAILAND
Faculty of Science, Naresuan University, Phitsanulok, THAILAND

I. Introduction

Principal component analysis (PCA) is one of the most powerful method used in image processing and pattern recognition applications to perform a specific task such as dimensionality reduction, image compression, and especially face recognition [1], [2]. The PCA method consists of two main steps-(I) computing covariance matrix and (II) extracting eigenvector and eigenvalue. In the first step, the covariance matrix is calculated from the data zero mean which is computed by the subtraction between input data and mean. In the second step, a set of eigenvectors and eigenvalues are extracted by using the covariance matrix based on linear transformation. The eigenvectors are sorted in descending order with respect to their corresponding eigenvalues. In case of face recognition, the eigenvectors selected from 1st to order are used to create the eigenface feature using the projection technique.

Faculty of Science, Naresuan University, Phitsanulok, THAILAND
Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, THAILAND
Faculty of Science, Naresuan University, Phitsanulok, THAILAND

References

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