The FERET evaluation methodology for face-recognition algorithms

Phillips, P. Jonathon; Moon, Hyeonjoon; Rizvi, Syed A.; Rauss, Patrick J.

doi:10.1109/34.879790

Cited by 4,044 publications

(878 citation statements)

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“…Results on FERET DatabaseFor the purpose of applications, we tested RS2DPCA on the FERET face database, which has been widely used to evaluate some face recognition methods [27]. In our experiments, two FERET image sets are used i.e.…”

Section: Results On Orl Databasementioning

confidence: 99%

Robust Sparse 2D Principal Component Analysis for Object Recognition

Meng¹,

Zheng²

2013

Appl. Math. Inf. Sci.

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Abstract:We extensively investigate robust sparse two dimensional principal component analysis (RS2DPCA) that makes the best of semantic, structural information and suppresses outliers in this paper. The RS2DPCA combines the advantages of sparsity, 2D data format and L1-norm for data analysis. We also prove that RS2DPCA can offer a good solution of seeking spare 2D principal components. To verify the performance of RS2DPCA in object recognition, experiments are performed on three famous face databases, i.e. Yale, ORL, and FERET, and the experimental results show that the proposed RS2DPCA outperform the same class of algorithms for face recognition, such as robust sparse PCA, L1-norm-based 2DPCA.

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Section: Results On Orl Databasementioning

confidence: 99%

Robust Sparse 2D Principal Component Analysis for Object Recognition

Meng¹,

Zheng²

2013

Appl. Math. Inf. Sci.

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“…Los reportes de evaluación de reconocimiento de rostros, por ejemplo Face Recognition Technology (FERET) (Phillips et al, 2000) indican que muchas de los resultados de los métodos del reconocimiento de rostros disminuye con cambios en la luz, postura y otros factores (Chellappa, Sirohey y Wilson, 1995;Valentin, Abdi, O'Toole, y Cottrell, 1994;Zhao y Rosenfeld, 2003).…”

Section: Introductionunclassified

Untitled

2017

riepg

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RESUMENEn el presente documento se expone una revisión de los métodos aplicados al problema de reconocimiento automático de rostros, se revisan los modelos holísticos, los basados en característica locales y los de métodos híbridos. Dentro de los métodos holísticos se revisan el Análisis de componentes Principales PCA y el Análisis Discriminatorio Lineal LDA. Dentro de los métodos basados en características locales se revisan los basados en las características de Gabor y Dentro de los métodos híbridos se revisa una propuesta que combina el PCA y las características de Gabor. Finalmente se presentan y explican las ventajas y desventajas de cada método así como los resultados publicados en los 4 artículos revisados objeto del presente.Ṕ alabras clave: Reconocimiento automático de rostros, métodos holísticos, métodos basados en características locales, métodos híbridos, ABSTRACTThis paper presents a review of the methods applied to the problem of automatic face recognition, we review the holistic models, those based on local features and those of hybrid methods. Within the holistic methods we review the Principal Components Analysis PCA y the Linear Discriminatory Analysis LDA. Within the methods based on local characteristics are reviewed those based on the characteristics of Gabor and Within the hybrid methods is revised a proposal that combines the PCA and the characteristics of Gabor. Finally, the advantages and disadvantages of each method as well as the results published in the 4 revised articles object of the present are presented and explained.

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“…Other databases we have obtained and used include the AR Face Database (Martinez and R., 1998), the BioID Face Database (Jesorsky et al, 2001), the Facial Recognition Technology Database (FERET) database (Phillips et al, 1998, Phillips et al, 2000, the Yale Face Databases A and B (Georghiades et al, 2001), and the AT&T Database of Faces (Samaria and Harter, 1994). Figure 3 and figure 4 shows a few images from two collections showing variation in lighting and pose/expression respectively.…”

Section: Face Databases and Validationmentioning

confidence: 99%