Deterioration of visual information in face classi

Authors: Gabriel Jarillo Alvarado Witold Pedrycz M Reformat KeunChang Kwak

Publish Date: 2006/03/11

Volume: 17, Issue: 1, Pages: 68-

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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 visàvis 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 wellknown 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 classifiersGabriel Jarillo Alvarado obtained his BSc degree in Biomedical Engineering from the Universidad Iberoamericana Mexico In 2003 he obtained his MSc degree from the University of Alberta at the Department of Electrical and Computer Engineering he is currently enrolled in the PhD program at the same University His research interests involve machine learning pattern recognition and evolutionary computation with particular interest to biometrics for personal identificationWitold 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 knowledgebased 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 EditorinChief of Information SciencesMarek Reformat received his MSc degree from Technical University of Poznan Poland and his PhD from University of Manitoba Canada His interests were related to simulation and modeling in timedomain 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 neurofuzzy 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 computingKeunChang Kwak received BSc MSc and PhD 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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