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Deterioration of visual information in face classification using Eigenfaces and Fisherfaces

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Abstract

In the area of biometrics, face classification becomes one of the most appealing and commonly used approaches for personal identification. There has been an ongoing quest for designing systems that exhibit high classification rates and portray significant robustness. This feature becomes of paramount relevance when dealing with noisy and uncertain images. The design of face recognition classifiers capable of operating in presence of deteriorated (noise affected) face images requires a careful quantification of deterioration of the existing approaches vis-à-vis anticipated form and levels of image distortion. The objective of this experimental study is to reveal some general relationships characterizing the performance of two commonly used face classifiers (that is Eigenfaces and Fisherfaces) in presence of deteriorated visual information. The findings obtained in our study are crucial to identify at which levels of noise the face classifiers can still be considered valid. Prior knowledge helps us develop adequate face recognition systems. We investigate several typical models of image distortion such as Gaussian noise, salt and pepper, and blurring effect and demonstrate their impact on the performance of the two main types of the classifiers. Several distance models derived from the Minkowski family of distances are investigated with respect to the produced classification rates. The experimental environment concerns a well-known standard in this area of face biometrics such as the FERET database. The study reports on the performance of the classifiers, which is based on a comprehensive suite of experiments and delivers several design hints supporting further developments of face classifiers.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, 9107-116 Street, Edmonton, Alberta, Canada

    Gabriel Jarillo Alvarado, Witold Pedrycz, M. Reformat & Keun-Chang Kwak

Authors
  1. Gabriel Jarillo Alvarado
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  2. Witold Pedrycz
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  3. M. Reformat
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  4. Keun-Chang Kwak
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Corresponding author

Correspondence to Gabriel Jarillo Alvarado.

Additional information

Gabriel Jarillo Alvarado obtained his B.Sc. degree in Biomedical Engineering from the Universidad Iberoamericana, Mexico. In 2003 he obtained his M.Sc. degree from the University of Alberta at the Department of Electrical and Computer Engineering, he is currently enrolled in the Ph.D. program at the same University. His research interests involve machine learning, pattern recognition, and evolutionary computation with particular interest to biometrics for personal identification.

Witold Pedrycz is a Professor and Canada Research Chair (CRC) in Computational Intelligence) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. His research interests involve Computational Intelligence, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 9 research monographs. Witold Pedrycz has been a member of numerous program committees of conferences in the area of fuzzy sets and neurocomputing. He currently serves on editorial board of numereous journals including IEEE Transactions on Systems Man and Cybernetics, Pattern Recognition Letters, IEEE Transactions on Fuzzy Systems, Fuzzy Sets & Systems, and IEEE Transactions on Neural Networks. He is an Editor-in-Chief of Information Sciences.

Marek Reformat received his M.Sc. degree from Technical University of Poznan, Poland, and his Ph.D. from University of Manitoba, Canada. His interests were related to simulation and modeling in time-domain, as well as evolutionary computing and its application to optimization problems For three years he worked for the Manitoba HVDC Research Centre, Canada, where he was a member of a simulation software development team. Currently, Marek Reformat is with the Department of Electrical and Computer Engineering at University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, as well as probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software and Knowledge Engineering. He has been a member of program committees of several conferences related to Computational Intelligence and evolutionary computing.

Keun-Chang Kwak received B.Sc., M.Sc., and Ph.D. degrees in the Department of Electrical Engineering from Chungbuk National University, Cheongju, South Korea, in 1996, 1998, and 2002, respectively. During 2002–2003, he worked as a researcher in the Brain Korea 21 Project Group, Chungbuk National University. His research interests include biometrics, computational intelligence, pattern recognition, and intelligent control.

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Alvarado, G.J., Pedrycz, W., Reformat, M. et al. Deterioration of visual information in face classification using Eigenfaces and Fisherfaces. Machine Vision and Applications 17, 68–82 (2006). https://doi.org/10.1007/s00138-006-0016-4

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  • DOI: https://doi.org/10.1007/s00138-006-0016-4

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