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Abstract

Multibiometric systems utilize the evidence presented by multiple biometric sources (e.g., face and fingerprint, multiple fingers of a user, multiple matchers, etc.) in order to determine or verify the identity of an individual. Information from multiple sources can be consolidated in several distinct levels, including the feature extraction level, match score level and decision level. While fusion at the match score and decision levels have been extensively studied in the literature, fusion at the feature level is a relatively understudied problem. In this paper fusion at the feature level in 3 different scenarios: (i) fusion of PCA and LDA coefficients of face; (ii) fusion of LDA coefficients corresponding to the R,G,B channels of a face image; (iii) fusion of face and hand modalities is discussed. Preliminary results are encouraging and help in highlighting the pros and cons of performing fusion at this level. The primary motivation of this work is to demonstrate the viability of such a fusion and to underscore the importance of pursuing further research in this direction. Partitioning around medoids (PAM) algorithm is used to partition the set of n invariant feature points of the face and palm print images into k clusters.

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How to Cite
R.Karthiga, & S.Mangai. (2013). FEATURE LEVEL FUSION USING FACE AND PALMPRINT BIOMETRICS FOR SECURED AUTHENTICATION. International Journal of Intellectual Advancements and Research in Engineering Computations, 1(1), 1–7. Retrieved from https://ijiarec.com/ijiarec/article/view/1236