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 (Hindustan College of Science and Technology, Mathura, India), Munish Khanna (Hindustan College of Science and Technology, Mathura, India) and Hitendra Garg (GLA University, Mathura, India)
Copyright: © 2020 |Pages: 21
DOI: 10.4018/IJISMD.2020010103


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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2. Prior Published Studies

We have gone through several studies which resemble the proposed work; some of the prominent work which resembles our work is discussed in this section. Authors (Jain, Ross, & Prabhakar, 2004) presented comparative approach among different traits and presents error rates in terms of False matched rate (FMR) and False Non-Matched Rate (FNMR). In the next study authors (Darwish, Zaki, Saad, Nassar, & Schaefer, 2010) proposed an effective approach to multimodal biometrics using face and fingerprint recognition where Facial image representation is performed using local binary pattern (LBP) textures, while fingerprints are recognised on the basis of minutiae extraction.

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