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Activity based Person Identification using Fuzzy Representation and Discriminant Learning

Tutkimustuotosvertaisarvioitu

Standard

Activity based Person Identification using Fuzzy Representation and Discriminant Learning. / Iosifidis, Alexandros; Tefas, Anastasios; Pitas, Ioannis.

julkaisussa: IEEE Transactions on Information Forensics and Security, Vuosikerta 7, Nro 2, 2012, s. 530-542.

Tutkimustuotosvertaisarvioitu

Harvard

Iosifidis, A, Tefas, A & Pitas, I 2012, 'Activity based Person Identification using Fuzzy Representation and Discriminant Learning', IEEE Transactions on Information Forensics and Security, Vuosikerta. 7, Nro 2, Sivut 530-542. https://doi.org/10.1109/TIFS.2011.2175921

APA

Iosifidis, A., Tefas, A., & Pitas, I. (2012). Activity based Person Identification using Fuzzy Representation and Discriminant Learning. IEEE Transactions on Information Forensics and Security, 7(2), 530-542. https://doi.org/10.1109/TIFS.2011.2175921

Vancouver

Iosifidis A, Tefas A, Pitas I. Activity based Person Identification using Fuzzy Representation and Discriminant Learning. IEEE Transactions on Information Forensics and Security. 2012;7(2):530-542. https://doi.org/10.1109/TIFS.2011.2175921

Author

Iosifidis, Alexandros ; Tefas, Anastasios ; Pitas, Ioannis. / Activity based Person Identification using Fuzzy Representation and Discriminant Learning. Julkaisussa: IEEE Transactions on Information Forensics and Security. 2012 ; Vuosikerta 7, Nro 2. Sivut 530-542.

Bibtex - Lataa

@article{d20f7d3cc2c64666bd7e9da4de19d798,
title = "Activity based Person Identification using Fuzzy Representation and Discriminant Learning",
abstract = "In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which ’walk’ (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy Vector Quantization and Linear Discriminant Analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.",
author = "Alexandros Iosifidis and Anastasios Tefas and Ioannis Pitas",
year = "2012",
doi = "10.1109/TIFS.2011.2175921",
language = "English",
volume = "7",
pages = "530--542",
journal = "IEEE Transactions on Information Forensics and Security",
issn = "1556-6013",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "2",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Activity based Person Identification using Fuzzy Representation and Discriminant Learning

AU - Iosifidis, Alexandros

AU - Tefas, Anastasios

AU - Pitas, Ioannis

PY - 2012

Y1 - 2012

N2 - In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which ’walk’ (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy Vector Quantization and Linear Discriminant Analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.

AB - In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which ’walk’ (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera setup is used to capture the human body from different viewing angles. Fuzzy Vector Quantization and Linear Discriminant Analysis are exploited in order to provide a discriminant activity representation. Person identification, activity recognition and viewing angle specification results are obtained for all the available cameras independently. By properly combining these results, a view-invariant activity-independent person identification method is obtained. The proposed approach has been tested in challenging problem setups, simulating real application situations. Experimental results are very promising.

U2 - 10.1109/TIFS.2011.2175921

DO - 10.1109/TIFS.2011.2175921

M3 - Article

VL - 7

SP - 530

EP - 542

JO - IEEE Transactions on Information Forensics and Security

JF - IEEE Transactions on Information Forensics and Security

SN - 1556-6013

IS - 2

ER -