Combination of Physiological and Behavioral Biometric for Human Identification

Emdad Hossain, Girija Chetty

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

Abstract

In this paper we propose a novel person-identification scheme based on gait biometric information in surveillance videos using simple PCA-LDA features, and RBF-MLP and SMO-SVM classifier. The experimental evaluation on resolution surveillance video images from a publicly available database [1] showed that the combined PCA-MLP and LDA-MLP technique turns out to be a powerful method for capturing identity specific information from walking gait patterns.
Original languageEnglish
Title of host publicationInternational Workshop on Machine Learning and Data Mining in Pattern Recognition
Subtitle of host publicationLecture Notes in Artificial Intelligence
EditorsPetra Perner
Place of PublicationBerlin, Germany
PublisherSpringer
Pages380-393
Number of pages14
Volume7376
ISBN (Electronic)9783642315374
ISBN (Print)9783642315367
DOIs
Publication statusPublished - 2012
Event8th International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012 - Berlin, Berlin, Germany
Duration: 13 Jul 201220 Jul 2012

Conference

Conference8th International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012
Abbreviated titleMLDM 2012
CountryGermany
CityBerlin
Period13/07/1220/07/12

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  • Cite this

    Hossain, E., & Chetty, G. (2012). Combination of Physiological and Behavioral Biometric for Human Identification. In P. Perner (Ed.), International Workshop on Machine Learning and Data Mining in Pattern Recognition: Lecture Notes in Artificial Intelligence (Vol. 7376, pp. 380-393). Springer. https://doi.org/10.1007/978-3-642-31537-4_30