Reviews, Refinements and New Ideas in Face Recognition 2011
DOI: 10.5772/18432
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Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition

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Cited by 8 publications
(8 citation statements)
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“…We decided to use a method based on LBP features with Chi-square distance as a similarity metric which is described in details in [5,12]. The selected method provides a trade-of between accuracy and computational complexity; thus it can run in real-time while maintaining state-ofthe-art recognition accuracy (13).…”
Section: Recognition Of a Patientmentioning
confidence: 99%
“…We decided to use a method based on LBP features with Chi-square distance as a similarity metric which is described in details in [5,12]. The selected method provides a trade-of between accuracy and computational complexity; thus it can run in real-time while maintaining state-ofthe-art recognition accuracy (13).…”
Section: Recognition Of a Patientmentioning
confidence: 99%
“…Two subsets (FERET-1 and FERET-2) are used to evaluate the performance of our method on SSS and SSPP face recognition, respectively. Similar to [ 7 ], FERET-1 contains all available subjects that have more than four frontal images. There are 665 images from 82 subjects in total.…”
Section: System Performance Analysismentioning
confidence: 99%
“…We have compared our method with several conventional face recognition algorithms without ensemble, including: PCA family [ 7 ], LDA family [ 32 ], LPP family (locality preserving projections) [ 32 ], CCA family (canonical correlation analysis) [ 6 ] and several other representative methods, such as SVM, neural network-based methods (MLP (multilayer perception) and RBF (radial basis function network)) [ 7 ]. For a fair comparison, we reported the performances of these methods presented in their published papers.…”
Section: System Performance Analysismentioning
confidence: 99%
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“…Facial recognition is a very common application of machine vision. There are many approaches to facial recognition, including both holistic and local comparisons [17].…”
Section: …………………………………………………………………………………………………… Introduction:-mentioning
confidence: 99%