Open AccessMultibiometrics System Design Based on Feature Level Fusion
Author(s) -
Suvarna Joshi,
Dinesh Kumar
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1368.109119
Subject(s) - biometrics , computer science , signature recognition , hamming distance , artificial intelligence , iris recognition , pattern recognition (psychology) , word error rate , palm print , feature extraction , classifier (uml) , face (sociological concept) , feature vector , feature (linguistics) , facial recognition system , algorithm , social science , linguistics , philosophy , sociology
An efficient multimodal biometric system which combines biometric data originated from face, iris and signature biometrics has been presented. Proposed feature extraction algorithm for unimodal and multimodal system has been based on discrete wavelet transform. Among the various biometrics face and iris based human authentication system are proved reliable and efficient. Signature as a behavioral biometrics is very important in financial transaction. Signature has highest variability among all biometrics. This research work proposes an approach to combine signature biometrics with face and iris biometric. Proposed method fuses biometric information originated from face, iris and signature at feature level. Hamming distance based classifier has been used for classifying feature vector as a genuine or imposter. Proposed multibiometrics system has been evaluated on chimeric databases. It has been shown by the reported results that proposed multimodal system outperforms unimodal system performance. Proposed system has been analyzed for recognition rates and error rates. Performance of proposed multimodal system shows improvement in recognition rate and reduction in error.