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2 results on '"ace recognition"'

1. Mixed Group Ranks: Preference and Confidence in Classifier Combination.

Author

Melnik, Ofer, Vardi, Yehuda, and Zhang, Cun-Hui

Subjects
*SET theory, *BIOMETRY, *LOGISTIC regression analysis, *REGRESSION analysis, *GUIDELINES, *MATHEMATICS
Abstract

Classifier combination holds the potential of improving performance by combining the results of multiple classifers. For domains with very large numbers of classes, such as biometrics, we present an axiomatic framework of desirable mathematical properties for combination functions of rank-based classifiers. This framework represents a continuum of combination rules, including the Borda Count, Logistic Regression, and Highest Rank combination methods as extreme cases [11], [23], [4], [13]. Intuitively, this framework captures how the two complementary concepts of general preference for specific classifiers and the confidence it has in any specific result (as indicated by ranks) can be balanced while maintaining consistent rank interpretation. Mixed Group Ranks (MGR) is a new combination function that balances preference and confidence by generalizing these other functions. We demonstrate that MGR is an effective combination approach by performing multiple experiments on data sets with large numbers of classes and classifiers from the FERET face recognition study. [ABSTRACT FROM AUTHOR]

Published
2004
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2. Decision optimization for face recognition based on an alternate correlation plane quantification metric

Author

Philippe Katz, Mohammad S. Alam, Ayman Alfalou, C. Brosseau, Laboratoire ISEN (L@BISEN), Institut supérieur de l'électronique et du numérique (ISEN)-YNCREA OUEST (YO), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Department of Electrical and Computer Engineering University of South Alabama, 6001 USA South Dr., Mobile, AL 36688-0002, USA, Department of Electrical and Computer Engineering, University of South Alabama-University of South Alabama, and Alfalou, Ayman

Subjects
[SPI.OPTI] Engineering Sciences [physics]/Optics / Photonic, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Image processing : Pattern recognition, Noise reduction, 02 engineering and technology, 01 natural sciences, ace recognition, Constant false alarm rate, 010309 optics, Correlation, 020210 optoelectronics & photonics, Optics, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Linear combination, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, metric, Mathematics, Image processing : Image recognition, business.industry, Plane (geometry), Filter (signal processing), Atomic and Molecular Physics, and Optics, Noise, algorithms and filters, (100.5010) (100.3008), POF filter, correlation, Metric (mathematics), [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm
Abstract

International audience; We consider a new approach for enhancing the discrimination performance of the VanderLugt correlator. Instead of trying to optimize the correlation filter, or propose a new decision correlation peak detection criterion, we propose herein to denoise the correlation plane before applying the peak-to-correlation energy (PCE) criterion. For that purpose, we use a linear functional model to express a given correlation plane as a linear combination of the correlation peak, noise, and residual components. The correlation peak is modeled using an orthonormalized function and the singular value decomposition method. A set of training correlation planes is then selected to create the correlation noise components. Finally, an optimized correlation plane is reconstructed while discarding the noise components. Independently of the filter correlation used, this technique denoises the correlation plane by lowering the correlation noise magnitude in case of true correlation and decreases the false alarm rate when the target image does not belong to the desired class. Test results are presented, using a composite filter and a face recognition application, to verify the effectiveness of the proposed technique.

Published
2012
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