الفهرس 
I. Introduction.Only 14 pages are availabe for public view
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Abstract
Most modern security systems depend on encryption and password
techniques in data transfer and on biometrics to secure the access to different
systems. These traditional systems have suffered for a long time from
hacking trials. Hence, the researchers have concentrated on biometric
systems to avoid these limitations. These biometric systems require the
generation of databases comprising the discriminating features extracted
from the biometrics. Unfortunately if the biometric databases have been
hacked and stolen, the biometrics saved in this system will be stolen forever.
Thus, there is a bad to develop new cancellable biometric systems. The basic
concept of cancellable biometrics is to use another version of the original
biometric template created through a 1-way transform of a high-security
encryption algorithms, which keeps the original biometrics safe and away
from utilization in the system. Recognition of the generated cancellable
biometrics is performed through feature from them and hence performing
the matching with the saved database. The main advantage of this trend is
that the original biometrics are kept safe and away from any hacking
attempts and from being stolen. If the database is kecked or stolen, the oneway
transform or the encryption technique may be changed. The main
research challenges in the development of cancellable biometrics systems
are the design of efficient one-way transforms, the design of efficient
encryption algorithms for biometrics, the set-up of an intelligent system for
cancellable biometric systems adopting new learning methodologies such as
deep learning. This thesis deals with the issue of cancellable biometrics and
presents two efficient algorithms for this purpose.