NDLI: A new framework for IRIS and fingerprint recognition using SVM classification and extreme learning machine based on score level fusion

Content Provider	IEEE Xplore Digital Library
Author	Sangeetha, S. Radha, N.
Copyright Year	2013
Description	Author affiliation: PSGR Krishnammal Coll. for Women, Coimbatore, India (Sangeetha, S.) \|\| G.R. Govindarajalu Sch. of Appl. Comput. Technol., Coimbatore, India (Radha, N.)
Abstract	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.
Sponsorship	IEEE Madras Sect.
Starting Page	183
Ending Page	188
File Size	464276
Page Count	6
File Format	PDF
ISBN	9781467343596
e-ISBN	9781467346030
DOI	10.1109/ISCO.2013.6481145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biometric Image recognition Extreme Learning Machine Score Level Fusion Support Vector Machine Statistics Multimodal Biometric Accuracy Databases Sociology Machine learning K-Mean Clustering Iris recognition Recognition System
Content Type	Text
Resource Type	Article

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