Signal processing for ubiquitous biometric systems
thesisposted on 22.05.2021, 09:36 by Danoush Hosseinzadeh
This work presents two hardware independent and ubiquitous biometric solutions that can significantly improve security for computer and telephone related applications. Firstly, for computer security, a GMM based keystroke verification method is proposed along with the up-up keystroke latency (UUKL) feature which is being used for the first time. This method can verify the identity of users based on their typing pattern and achieved a FAR of 5.1%, a FRR of 6.5%, and a EER of 5.8% for a database of 41 users. Due to many inconsistencies in previous works, a new keystroke protocol has also been proposed. This protocol makes a number of recommendations concerning how to improve performance, reliability, and accuracy of any keystroke recognition system. Secondly, a GMM based text-independent speaker identification scheme is also proposed that utilizes novel spectral features for better speaker discrimination. Based on 100 users from the TIMIT database, these features achieved an identification error of 1.22% by incorporating information about the source of the speech signal. This represents a 6% improvement over the MFCC based features.