Multibiometrics System Design Based on Feature Level Fusion
Suvarna Joshi1, Abhay Kumar2

1Suvarna C. Joshi*, Senior Assistant Professor, Balaji Institute of Telecom and Management, Pune, India.
2Dr.Abhay Kumar, Professor & HOD, School of Electronics, Devi Ahilya University, Indore, Madyapradesh, India.
Manuscript received on September 11, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 4127-4132 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1368109119/2019©BEIESP | DOI: 10.35940/ijeat.A1368.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: 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.
Keywords: Feature fusion, Face, Iris, Signature, Wavelet transform.