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Author: search.filters.author.Radha NSubject: search.filters.subject.Unimodal

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    A SURVEY ON FUSION TECHNIQUES FOR MULTIMODAL BIOMETRIC IDENTIFICATION
    (International Journal of Innovative Research in Computer and Communication Engineering, 2015-01) Sorubasree S.R; Radha N
    Biometric is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data and comparing this set against to the template set in the database. Biometric systems based on single source of information are called unimodal biometric system. The performance of unimodal system is affected by noisy sensor data and non-universality. Problems raised in the unimodal system can be resolved using multimodal biometric. Multimodal biometrics can be achieved through a fusion of two or more images, where the resultant fused image will be more secured. This paper discusses various fusion techniques that are used in multimodal biometrics.
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    FUSION OF IRIS AND RETINA USING RANK-LEVEL FUSION APPROACH
    (International Journal of Computer Technology and Application, 2011) Kavitha A; Radha N
    Personal identification and authentication is difficulty in all the systems. Shared secrets like Personal Identification Numbers or Passwords and key devices such as Smart Cards are not presently sufficient in few situations. These traditional tokens-based systems may be easily stolen or lost. Biometrics is the only way of improving the capability to recognize the persons according to the physiological or behavioral features. In many real-world applications, unimodal biometric system suffers from some limitations of noise in sensed data, intra-class variation, inter-class similarities, non-universality and spoof attacks. Multibiometric systems seek to alleviate some of these problems by consolidating the evidence obtained from different sources. These systems help to achieve an increase in performance. This paper focused on developing a multimodal biometrics system, which uses biometrics such as iris and retina. Fusion of biometrics is performed by means of rank level fusion. The ranks of individual matchers are integrated using the borda count, and logistic regression approaches. The developed multimodal biometric system utilizes and Fisher’s Linear Discriminant (FLD) and Principal Component Analysis (PCA) methods for individual matchers (Iris and Retina) identification. The features from the biometrics are obtained by using the Fisher face. The experimental result shows the performance of the proposed multimodal biometrics system.