Reliable user authentication has become very important with rapid advancements in networking with increased concerns about security. Biometric systems perform recognition with the help of specific physiological or behavioral characteristics(s) of a person. Biometrics establishes identity on the basis of biological characters e.g., structure of your DNA, facial features, voice, gait etc, instead of ID cards, PIN numbers, tokens, passwords, etc. Uni-Biometrics systems depend on the evidence of only one source of information whereas multi-biometric systems consolidate/combine multiple sources of biometric evidences. Multi-biometric systems are capable of enhancing the matching performance as they get the evidence presented by different modalities or biological characteristics and the use of multiple body traits improves the identification accuracy significantly. Moreover, they are expected to increase population coverage, prevent spoofing attacks and provide fault tolerance to biometric systems. In this thesis, we have proposed an evolutionary approach to enhance the matching performance of our multi-biometric system. The system uses Discrete Cosine Transform at feature extraction level due to its high energy compaction property. The existing methods of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are also used in parallel with Discrete Cosine Transform (DCT) in order to compare the three feature extraction methods. LDA being more efficient than PCA as LDA uses both intra-class scatter and inter-class variation whereas PCA only deals with intra-class scatter. However, DCT offers far less computational complexity than the other two methods. Three biometric traits i.e. face, iris and ear have been used which are fused at feature level and genetic algorithm has been incorporated for feature vector optimization. Classification is performed through the Bayesian classifier. Results have been computed on the basis of Error Equal Rate (EER) values and ROC curves which have shown that the use of Discrete Cosine Transform with genetic algorithm has significantly improved the performance of our multi-biometric system.

TITLE - MULTIMODAL BIOMETRIC SECURITY USING EVOLUTIONARY COMPUTATION
AUTHOR - QURRAT UL AIN
••••••IJSER Edition - November 2014

UNIVERSITY - International Islamic University Islamabad
GUIDE NAME -
AYYAZ HUSSAIN