Feature Level Fusion of Iris and Voice.
Sameh, S., Moustafa, H., Zaki, F. (2021). Feature Level Fusion of Iris and Voice.. EKB Journal Management System, 40(5), 55-63. doi: 10.21608/bfemu.2020.96269
Sally Mohamed Sameh; Hossam El-Din Salah Moustafa; Fayez Wanis Zaki. "Feature Level Fusion of Iris and Voice.". EKB Journal Management System, 40, 5, 2021, 55-63. doi: 10.21608/bfemu.2020.96269
Sameh, S., Moustafa, H., Zaki, F. (2021). 'Feature Level Fusion of Iris and Voice.', EKB Journal Management System, 40(5), pp. 55-63. doi: 10.21608/bfemu.2020.96269
Sameh, S., Moustafa, H., Zaki, F. Feature Level Fusion of Iris and Voice.. EKB Journal Management System, 2021; 40(5): 55-63. doi: 10.21608/bfemu.2020.96269

Feature Level Fusion of Iris and Voice.

MEJ- Mansoura Engineering Journal
Article 6, Volume 40, Issue 5, December 2015, Page 55-63  XML PDF (848.77 K)
Document Type: Research Studies
DOI: 10.21608/bfemu.2020.96269
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Authors
Sally Mohamed Sameh1; Hossam El-Din Salah Moustafa email 1; Fayez Wanis Zaki2
1Electronics and Communications Engineering Department , Faculty of Engineering, Mansoura University, Mansoura, Egypt
2Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Abstract
Biometric systems are vastly used by various organizations for different security applications. The main use of such systems is in authentication and identification applications which including computer network login, passport control, corpse identification, electronic data security, terrorist identification, Internet access. Unimodal biometric systems suffer from a lot of problems like high error rate, low performance and imposters’ attack so the demand of Multimodal biometric systems takes place. Multimodal biometric systems have several advantages over unimodal biometric systems such as non-universality, larger population coverage, lower error rates, higher performance and higher genuine acceptance rates. In this paper, a study of multimodal fusion of voice and Iris at feature level is presented. The features are extracted from the voice signals using Power-Normalized Cepstral Coefficients (PNCC) and from the preprocessed iris images using Single Value Decomposition (SVD). The experiment have been done using samples collected from faculty of Engineering , Mansoura university for voice and CASIA iris database  for iris which gave accuracy of 98.4% after fusion. This result is acceptable and gives high accuracy than using voice and iris individually.
Keywords
Multimodal; Feature level fusion; voice recognition; iris recognition
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