Face recognition: reduced image Eigenfaces method

Chichizola, Franco; Giusti, Laura De; Giusti, Armando De; Naiouf, Marcelo

doi:10.1109/elmar.2005.193666

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Even though above techniques have been successfully applied to the task of face recognition, they do have certain disadvantages in terms of face recognition rate. Chichizola, F. [5] improved the recognition rate by transforming face images into smaller ones in order to allow working directly with the covariance matrix instead of using an approximation of it. Robustness for face recognition is measured using Gaussian classifier [9].The CBR module compares the similarity of the new face case with previously stored face cases and retrieves the most similar case to improve the recognition rate [6].…”

Section: Related Workmentioning

confidence: 99%

DCT-based unique faces for face recognition using Mahalanobis distance

Maheshkar

Kamble

Agarwal

et al. 2010

Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia

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In this paper, we propose a technique to generate DCT based unique normalized face using Principal Component Analysis (PCA). The idea of the PCA is to decompose face images into a small set of characteristic feature images. In the proposed technique we generate feature image by finding the peak values in the absolute DCT matrix followed by normalization. This maximizes the scatter between training dataset to give more discriminating power. The feature images so generated are called unique normalized faces as each image is different and unique from all other training faces. They have high recognition performance since they capture the global features onto a low dimensional linear "face space" extracted from the individual face of training dataset. We use Mahalanobis distance to measure the recognition between original face and the test face. The algorithm is tested on ORL face datasets. In the proposed technique we improved face recognition rate as compared to Eigenface, DCT-normalization and Wavelet-Denoising.

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Section: Related Workmentioning

confidence: 99%

DCT-based unique faces for face recognition using Mahalanobis distance

Maheshkar

Kamble

Agarwal

et al. 2010

Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia

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show abstract

“…To improve the face recognition rate, the authors in [15], [16], and [21] applied preprocessing technique before learning subspace from the original images. Down-sampling is considered as preprocessing step in [21]. Whitening filter is used as a preprocessing step in [15].…”

Section: Related Workmentioning

confidence: 99%

Gradient-Orientation-Based PCA Subspace for Novel Face Recognition

Ghinea

Kannan

Kannaiyan³

2014

IEEE Access

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Face recognition is an interesting and a challenging problem that has been widely studied in the field of pattern recognition and computer vision. It has many applications such as biometric authentication, video surveillance, and others. In the past decade, several methods for face recognition were proposed. However, these methods suffer from pose and illumination variations. In order to address these problems, this paper proposes a novel methodology to recognize the face images. Since image gradients are invariant to illumination and pose variations, the proposed approach uses gradient orientation to handle these effects. The Schur decomposition is used for matrix decomposition and then Schurvalues and Schurvectors are extracted for subspace projection. We call this subspace projection of face features as Schurfaces, which is numerically stable and have the ability of handling defective matrices. The Hausdorff distance is used with the nearest neighbor classifier to measure the similarity between different faces. Experiments are conducted with Yale face database and ORL face database. The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches.

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Perfect histogram matching PCA for face recognition

Sevcenco

2010

Multidim Syst Sign Process

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Face recognition: reduced image Eigenfaces method

Cited by 4 publications

References 3 publications

DCT-based unique faces for face recognition using Mahalanobis distance

DCT-based unique faces for face recognition using Mahalanobis distance

Gradient-Orientation-Based PCA Subspace for Novel Face Recognition

Perfect histogram matching PCA for face recognition

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