Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TLDR
Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.Abstract:
Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.read more
Citations
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Flexible multi-classifier architecture for face recognition systems
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Automatic Face Annotation in Personal Photo Collections Using Context-Based Unsupervised Clustering and Face Information Fusion
TL;DR: A novel face annotation framework is proposed that systematically leverages context information such as situation awareness information with current face recognition (FR) solutions and significantly outperforms traditional face annotation solutions at no additional computational cost.
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Tensor Sparse Coding for Positive Definite Matrices
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Proceedings ArticleDOI
Large-scale adaptive semi-supervised learning via unified inductive and transductive model
De Wang,Feiping Nie,Heng Huang +2 more
TL;DR: This paper proposes an adaptive semi-supervised learning model that avoids the huge computational cost required by previous methods, and achieves a computational complexity linear to the number of data points, and is scalable to large-scale data.
References
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Baback Moghaddam,Alex Pentland +1 more
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