Khalid Saeed

Khalid Saeed Dr. Khalid Saeed, DSc, PhD, MSc, BSc Engg.

Professor of Computer Science at AGH
Faculty of Physics and Applied Computer Science,
AGH University of Science and Technology
Cracow, Poland

DVP term expires December 2016

Khalid Saeed received the BSc Degree in Electrical and Electronics Engineering in 1976 from Baghdad University in 1976, the MSc and PhD Degrees from Wroclaw University of Technology, in Poland in 1978 and 1981, respectively. He received his DSc Degree (Habilitation) in Computer Science from Polish Academy of Sciences in Warsaw in 2007. He is a Professor of Computer Science with AGH University of Science and Technology in Poland. He has published more than 120 publications – 17 edited books and 7 text and reference books. He supervised about 30 BSc projects, 90 MSc and 9 PhD theses. His areas of interest are Image Analysis and Processing, Biometrics and Computer Information Systems.


Biometrics and Ubiquitous Computing

Biometrics mainly associates with security tasks. However, there still are many other fields of interests where the traditional ways of human identification fail to serve for the intended purpose. These are, for example, Human comfort: (hotel, shopping, … where we could feel more comfortable if a system scans us for a positive use – recognizing us as clients for some discount or to automatically charge our accounts after purchasing some goods); Human healthcare: We have been developing our technology for advanced technology and money, but not
much considered our human-based philosophy and hence, we have to think about human based technology and philosophy – biometrics should develop in this direction; Mobile functions; Ubiquitous Computing and its applications; Emotion Detection and Kansei Engineering. These and many other aspects form the main goal of the presentation. I do not agree with limiting the Biometrics to the biometric passport or the applications of Biometrics in the airport, security, recognition of odd people. Why not think of extending the BM to recognize the gesture of people for different varieties of emotions? Why not put a camera or cameras and microphones in cars to take photos to passengers when an accident takes place and hence to inform the nearest hospital or a mobile phone about the state of passengers? In my opinion then Biometrics, in both its physiological and behavioral types, relates and should act along with other engineering sciences and philosophies to make them more general
and universal in their every day's use.


Simple Mathematical Models for Biometric Image Description


Biometrics is a science that deals with human identification on the basis of our biological features. Therefore, Biometrics belongs to Pattern Recognition and is part of it. Biometric examples are all features we are born with like facial image, finger-prints, iris of the eye, ... or the features we learn in our life like the way we write (signature), the way we walk (gait) or any of the behavioural characteristics. One of the basic steps in the procedure of pattern recognition for the right decision taken with high success rates of identification and verification is the way we furnish the characteristic points of the human biometric images. Once the biometric image is represented by the actual description, easy for implementation in the available popular computing systems, the recognition results will then be more satisfying. The characteristic points should cover all the essential information carried by the selected features that are necessary and in high percentage sufficient for human identification and/or verification.

The main aspect about the author's approach I show how to transform a collection of points (in the shape of noise, as can be seen in the figure below) to pattern that can be given in the form of feature vector.   



Detailed information on this transformation can be seen in:

Saeed K. (2014) Carathéodory–Toeplitz based mathematical methods and their algorithmic applications in biometric image processing, Applied Numerical Mathematics, Elsevier, APNUM 75, pp. 2-21.


Speech and Speaker Verification

The main objective of the paper is to show how a new voice recognition approach is used for person recognition by identifying their voice. The tool used in this approach is a mathematical model based mainly on Töeplitz Forms and Burg's estimation models. After filtering, the recorded voice signal is processed to get its power spectrum estimation. The feature vector is derived from the power spectrum and its adjacent plots and Töeplitz matrices. This vector has proved to furnish a unique unrepeated print –each individual has his/her own voice print. In the work presentation, the author introduces some experiments performed in MATLAB to show how this voiceprint looks like and how it is verified for recognition. The basic idea is derived from applying Töeplitz matrix minimal eigenvalues algorithm to Burg's estimating model. This implies a graphical approach for feature extraction, selection and hence signal-image description confronting the conventional and traditional methods.

Details are given in:
Saeed K., Nammous M. K., "A Speech-and-Speaker Identification System: Feature Extraction,
Description, and Classification of Speech-Signal Image," IEEE Trans. on Industrial
Electronics, vol. 54, no. 2 (2007) 887-897.