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Bicego, A. Farinelli F. Recchia M.
Behavioural biometrics using electricity load profiles Journal Article
In: Proceedings of the International Conference on Pattern Recognition, 2014.
Abstract | Links | BibTeX | Tags: Energy, Pattern Recognition
@article{bicego:etal:2014,
title = {Behavioural biometrics using electricity load profiles},
author = {A. Farinelli F. Recchia M. Bicego},
url = {https://www.sramchurn.com/wp-content/uploads/2014/10/CR_v1.pdf},
year = {2014},
date = {2014-08-24},
journal = {Proceedings of the International Conference on Pattern Recognition},
abstract = {Modelling behavioural biometric patterns is a key
issue for modern user centric applications, aimed at better monitoring
users’ activities, understanding their habits and detecting
their identity. Following this trend, this paper investigates whether
the electrical energy consumption of a user can be a distinctive
behavioural biometric trait. In particular we analyse daily and
weekly load profiles showing that they are closely related to
the identity of the users. Hence, we believe that this level of
analysis can open interesting application scenarios in the field of
energy management and it provides a good working framework
for the continuous development of smart environments with
demonstrable benefits on real-world implementations.},
keywords = {Energy, Pattern Recognition},
pubstate = {published},
tppubtype = {article}
}
Bicego, A. Farinelli F. Recchia M.
Behavioural biometrics using electricity load profiles Journal Article
In: Proceedings of the International Conference on Pattern Recognition, 2014.
@article{bicego:etal:2014,
title = {Behavioural biometrics using electricity load profiles},
author = {A. Farinelli F. Recchia M. Bicego},
url = {https://www.sramchurn.com/wp-content/uploads/2014/10/CR_v1.pdf},
year = {2014},
date = {2014-08-24},
journal = {Proceedings of the International Conference on Pattern Recognition},
abstract = {Modelling behavioural biometric patterns is a key
issue for modern user centric applications, aimed at better monitoring
users’ activities, understanding their habits and detecting
their identity. Following this trend, this paper investigates whether
the electrical energy consumption of a user can be a distinctive
behavioural biometric trait. In particular we analyse daily and
weekly load profiles showing that they are closely related to
the identity of the users. Hence, we believe that this level of
analysis can open interesting application scenarios in the field of
energy management and it provides a good working framework
for the continuous development of smart environments with
demonstrable benefits on real-world implementations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bicego, A. Farinelli F. Recchia M.
Behavioural biometrics using electricity load profiles Journal Article
In: Proceedings of the International Conference on Pattern Recognition, 2014.
Abstract | Links | BibTeX | Tags: Energy, Pattern Recognition
@article{bicego:etal:2014,
title = {Behavioural biometrics using electricity load profiles},
author = {A. Farinelli F. Recchia M. Bicego},
url = {https://www.sramchurn.com/wp-content/uploads/2014/10/CR_v1.pdf},
year = {2014},
date = {2014-08-24},
journal = {Proceedings of the International Conference on Pattern Recognition},
abstract = {Modelling behavioural biometric patterns is a key
issue for modern user centric applications, aimed at better monitoring
users’ activities, understanding their habits and detecting
their identity. Following this trend, this paper investigates whether
the electrical energy consumption of a user can be a distinctive
behavioural biometric trait. In particular we analyse daily and
weekly load profiles showing that they are closely related to
the identity of the users. Hence, we believe that this level of
analysis can open interesting application scenarios in the field of
energy management and it provides a good working framework
for the continuous development of smart environments with
demonstrable benefits on real-world implementations.},
keywords = {Energy, Pattern Recognition},
pubstate = {published},
tppubtype = {article}
}
Bicego, A. Farinelli F. Recchia M.
Behavioural biometrics using electricity load profiles Journal Article
In: Proceedings of the International Conference on Pattern Recognition, 2014.
@article{bicego:etal:2014,
title = {Behavioural biometrics using electricity load profiles},
author = {A. Farinelli F. Recchia M. Bicego},
url = {https://www.sramchurn.com/wp-content/uploads/2014/10/CR_v1.pdf},
year = {2014},
date = {2014-08-24},
journal = {Proceedings of the International Conference on Pattern Recognition},
abstract = {Modelling behavioural biometric patterns is a key
issue for modern user centric applications, aimed at better monitoring
users’ activities, understanding their habits and detecting
their identity. Following this trend, this paper investigates whether
the electrical energy consumption of a user can be a distinctive
behavioural biometric trait. In particular we analyse daily and
weekly load profiles showing that they are closely related to
the identity of the users. Hence, we believe that this level of
analysis can open interesting application scenarios in the field of
energy management and it provides a good working framework
for the continuous development of smart environments with
demonstrable benefits on real-world implementations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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Bicego, A. Farinelli F. Recchia M.
Behavioural biometrics using electricity load profiles Journal Article
In: Proceedings of the International Conference on Pattern Recognition, 2014.
@article{bicego:etal:2014,
title = {Behavioural biometrics using electricity load profiles},
author = {A. Farinelli F. Recchia M. Bicego},
url = {https://www.sramchurn.com/wp-content/uploads/2014/10/CR_v1.pdf},
year = {2014},
date = {2014-08-24},
journal = {Proceedings of the International Conference on Pattern Recognition},
abstract = {Modelling behavioural biometric patterns is a key
issue for modern user centric applications, aimed at better monitoring
users’ activities, understanding their habits and detecting
their identity. Following this trend, this paper investigates whether
the electrical energy consumption of a user can be a distinctive
behavioural biometric trait. In particular we analyse daily and
weekly load profiles showing that they are closely related to
the identity of the users. Hence, we believe that this level of
analysis can open interesting application scenarios in the field of
energy management and it provides a good working framework
for the continuous development of smart environments with
demonstrable benefits on real-world implementations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}