Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition

Emdad Hossain, Girija CHETTY

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

12 Citations (Scopus)

Abstract

In this paper we propose a novel multimodal feature learning technique based on deep learning for gait biometric based human-identification scheme from surveillance videos. Experimental evaluation of proposed learning features based on novel deep learning and standard (PCA/LDA) features in combination with classifier techniques (NN/MLP/SVM/SMO) on different datasets from two gait databases (the publicly available CASIA multiview multispectral database, and the UCMG multiview database), show a significant improvement in recognition accuracies with proposed fused deep learning features.
Original languageEnglish
Title of host publication20th International Conference on Neural Information Processing (ICONIP 2013)
Subtitle of host publicationLecture Notes in Computer Science
EditorsMinho Lee, Akira Hirose, Zeng-Guang, Hou, Rhee Man Kil
Place of PublicationGermany
PublisherSpringer
Pages721-728
Number of pages8
Volume8227
ISBN (Electronic)9783642420429
ISBN (Print)9783642420412
DOIs
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Conference

Conference20th International Conference on Neural Information Processing (ICONIP 2013)
Abbreviated titleICONIP 2013
CountryKorea, Republic of
CityDaegu
Period3/11/137/11/13

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Cite this

Hossain, E., & CHETTY, G. (2013). Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition. In M. Lee, A. Hirose, Zeng-Guang, Hou, & R. M. Kil (Eds.), 20th International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science (Vol. 8227, pp. 721-728). Germany: Springer. https://doi.org/10.1007/978-3-642-42042-9_89