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Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Law Kumar Singh, Munish Khanna, Hitendra Garg
Copyright: © 2020 |Volume: 11 |Issue: 1 |Pages: 21
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781799806851|DOI: 10.4018/IJISMD.2020010103
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MLA

Singh, Law Kumar, et al. "Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features." IJISMD vol.11, no.1 2020: pp.37-57. http://doi.org/10.4018/IJISMD.2020010103

APA

Singh, L. K., Khanna, M., & Garg, H. (2020). Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features. International Journal of Information System Modeling and Design (IJISMD), 11(1), 37-57. http://doi.org/10.4018/IJISMD.2020010103

Chicago

Singh, Law Kumar, Munish Khanna, and Hitendra Garg. "Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features," International Journal of Information System Modeling and Design (IJISMD) 11, no.1: 37-57. http://doi.org/10.4018/IJISMD.2020010103

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Abstract

Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.

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