m) 2013 - 19 Documents

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Author: search.filters.author.Radha, NHas files: Yes

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    SECURING IRIS AND FINGERPRINT TEMPLATES USING FUZZY VAULT AND SYMMETRIC ALGORITHM
    (IEEE, 2013-03-21) Sowkarthika, S; Radha, N
    The important aspect of all verification system is authentication and security. This aspect necessitates the development of a method that ensures user security and privacy. The traditional methods such as tokens and passwords provide security to the users. Uncertainly, the attackers can easily compromise these techniques. In recent years, the combination of biometrics and cryptography techniques has been proved as a efficient way to achieve security. The important feature of using biometric template is that it cannot be exploit by an unauthorized user. Most commonly used biometric features are iris, retina, fingerprint, face, palmprint, hand geometry, voice and so on. Fuzzy vault is the concept which uses the combination of biometrics and cryptographic key generation technique. This fuzzy vault act as a additional layer of security. This paper proposes a biometric verification system investigating the combined usage of multimodal biometric features and fuzzy vault scheme. This approach uses of fingerprint and iris in order to provide higher accuracy rate. Experiments were conducted to investigate the performance of the proposed system in ensuring the user security and privacy.
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    A NEW FRAMEWORK FOR IRIS AND FINGERPRINT RECOGNITION USING SVM CLASSIFICATION AND EXTREME LEARNING MACHINE BASED ON SCORE LEVEL FUSION
    (IEEE, 2013-03-21) Sangeetha, S; Radha, N
    In a Multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. Two biometric characteristics are considered in this study: iris and fingerprint. Multimodal biometric system needs an effective fusion scheme to combine biometric characteristics derived from one or more modalities. The score level fusion is used to combine the characteristics from different biometric modalities. Fusion at the score level is a new technique, which has a high potential for efficient consolidation of multiple unimodal biometric matcher outputs. Support vector machine and extreme learning techniques are used in this system for recognition of biometric traits. In this, the Fingerprint-Iris system provides better performance and comparison of support vector machine and extreme learning machine based on score-level fusion methods is obtained. In score-level fusion, ELM provides better performance as compare to the SVM. It reduces the classification time of current system. This work is valuable and makes an efficient accuracy in such applications. This system can be utilized for person identification in several applications.
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    BAYESIAN CLASSIFICATION FOR IMAGE RETRIEVAL USING VISUAL DICTIONARY
    (Springer Link, 2013) Nazirabegum, M K; Radha, N
    Image Retrieval is one of the most promising technologies for retrieving images through the query image. It enables the user to search for the images based upon the relevance of the query image. The main objective of this paper is to develop a faster and more accurate image retrieval system for a dynamic environment such as World Wide Web (WWW). The image retrieval is done by considering color, texture, and edge features. The bag-of-words model can be applied to image classification, by treating image features as words. The goal is to improve the retrieval speed and accuracy of the image retrieval systems which can be achieved through extracting visual features. The global color space model and dense SIFT feature extraction technique have been used to generate a visual dictionary using Bayesian algorithm. The images are transformed into set of features. These features are used as an input in Bayesian algorithm for generating the code word to form a visual dictionary. These code words are used to represent images semantically to form visual labels using Bag-of-Features (BoF). Then it can be extended by combining more features and their combinations. The color and bitmap method involves extracting only the local and global features such as mean and standard deviation. But in this classification technique, color, texture, and edge features are extracted and then Bayesian Algorithm is applied on these image features which gives acceptable classification in order to increases the accuracy of image retrieval.