Plenary Lecture

Computational Intelligence Solutions for Biometrics

Professor Victor-Emil Neagoe
Depart. of Electronics, Telecommnunications and Information Technology,
Polytechnic University of Bucharest,
Splaiul Independentei 313, Bucharest, Romania

Abstract: The word biometrics is a combination of the Greek words bio and metric. When combined, it means “life measurement.” Biometrics concerns the study of automated methods for identifying an individual by measuring one or more physical or behavioral features of him. Certain physical human features or behaviors are characteristics that are specific and can be uniquely associated to one person. Common physiological biometric traits include: fingerprints, hand geometry, retina, iris, DNA and facial images. Whereas, common behavioral biometric traits include: handwriting, voice print, gait, and keystroke rhythms.
Nowadays biometrics is rapidly evolving; it becomes more and more attractive and effective in critical applications, such as to create safe personal IDs, to control the access to personal information or physical areas, to recognize terrorists or criminals, to study the movements of people, and to monitor the human behavior. Several governments are now using or will soon be using biometric technology. The U.S. INSPASS immigration card and the Hong Kong ID card, for example, both store biometric features for authentication.
Computational intelligence (CI) is a fastmoving research field with approaches primarily based on neural networks, machine learning, fuzzy logic, genetic algorithms and evolutionary computing. Computational intelligence (CI) technologies are robust, can be successfully applied to complex problems, are
efficiently adaptive, and usually have a parallel computational architecture. For those reasons they have been proved to be effective and efficient in biometric
feature extraction and biometric matching tasks, sometimes used in combination with traditional methods.
In this lecture we survey two kinds of major applications of CI in biometric technologies: CI-based feature extraction and CI-based biometric matching. We also present the original contribution of the author regarding some CI solutions for facial image recognition and iris identification.

Brief Biography of the Speaker:
Dr. Victor-Emil Neagoe is a Professor of the Department of Electronics, Telecommunications, and Information Technology at the Polytechnic University of Bucharest, Romania.
He teaches the following courses : Pattern Recognition and Artificial Intelligence; Digital Signal Processing; Computational Intelligence ; Detection and Estimation for Information Processing. He co-ordinates 12 Ph.D. candidates.
His research interest corresponds to the fields of pattern recognition, computational intelligence, biometric technology , satellite image analysis and sampling theory.
Prof. Neagoe is author of more than 110 published papers.
His has internationally recognized results concerning concurrent self-organized maps, face recognition, optimum color conversion, syntactical self-organized maps, nonuniform sampling theorems, inversion of the Van der Monde matrix, predictive ordering and linear approximation for image data compression, Legendre descriptors for classification of polygonal closed curves.
He has been included in Who’s Who in the World and Europe 500 and he has been nominated by the American Biographical Institute for American Medal of Honor and for World Medal of Honor.
He has been a Member IEEE since 1978 and a Senior Member IEEE since 1984.