Efficient face recognition based on weighted matrix distance metrics and 2DPCA algorithm

Rouabhia, Chahrazed; Tebbikh, Hicham

doi:10.2478/v10170-010-0040-5

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…The majority of algorithms for characteristic extraction used in face recognition are based on statistical methods: Principal Component Analysis (PCA) (Chan et al, 2010;Kirby and Sirovich, 1990;Perlibakas, 2004;Turk and Pentland, 1991); bidimensional PCA with dimension reduction in one direction (2DPCA) (Rouabhia and Tebbikh, 2011;Yang et al, 2004); bi-dimensional PCA with dimension reduction in two directions ((2D) 2 PCA) (Zhang and Zhou, 2005); Linear Discriminant Analysis (LDA) (Belhumeur et al, 1997;Chan et al, 2010); bi-dimensional LDA with dimension reduction in one direction (2DLDA) (Yang et al, 2005) and bidimensional LDA with dimension reduction in two directions ((2D) 2 LDA) (Noushath et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Proposing the novelty classifier for face recognition

Filho

Falcão

Costa

et al. 2014

Rev. Bras. Eng. Bioméd.

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Introduction: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifi er is proposed for face recognition: the novelty classifi er. Methods:The performance of a novelty classifi er is compared with the performance of the nearest neighbor classifi er. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. Results:In identifi cation mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verifi cation mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identifi cation mode is equal to 98.5%, and in the verifi cation mode, 88%. Conclusion: For face recognition, the novelty classifi er performs comparable to the best results already published in the literature, which further confi rms the novelty classifi er as an important pattern recognition method in biometry.

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Section: Introductionmentioning

confidence: 99%

Proposing the novelty classifier for face recognition

Filho

Falcão

Costa

et al. 2014

Rev. Bras. Eng. Bioméd.

View full text Add to dashboard Cite

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2DPCA versus PCA for face recognition

Tan

Luan

et al. 2015

J. Cent. South Univ.

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Dimensionality reduction methods play an important role in face recognition. Principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference (CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when 2DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.

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