One shot learning approach to identify drivers

Malik Haddad; David Sanders; Martin Clifford Langner; Giles Tewkesbury

doi:10.1007/978-3-030-82193-7_41

One shot learning approach to identify drivers

Malik Haddad, David Sanders, Martin Clifford Langner, Giles Tewkesbury

School of Mechanical & Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This paper presents a new approach to identify users of shared powered mobility platfroms using One Shot learning algorithm. An electronic circuit is created using a Raspberry Pi and a camera module. Python programming language is used to create a program to control the function of the camera module and conduct One Shot learning to identify users. A user interface containing Start and Stop buttons is created to operate the camera module. If Start is pressed, the Python program will trigger the camera to take a snapshot, then the program will identify the face in that snapshot and perform facial recognition to compare that face with a database of user facial images. If a match was found a message box popped-up showing the user name. Once a user is identified, their settings and preferences can be downloaded to the shared powered mobility platform. If the face in the snapshot did not match any of the user facial images in the data base, a message popped-up showing that user was not found in the database. Practical testing showed the system behaved satisfactorily and successfully detected users. One Shot learning allowed new users to be added to the database without the need to retrain the whole system.

Original language	English
Title of host publication	Proceedings of SAI Intelligent Systems Conference
Subtitle of host publication	IntelliSys 2021: Intelligent Systems and Applications
Editors	K. Arai
Publisher	Springer
Pages	622-629
Number of pages	8
ISBN (Electronic)	9783030821937
ISBN (Print)	9783030821920
DOIs	https://doi.org/10.1007/978-3-030-82193-7_41
Publication status	Early online - 4 Aug 2021
Event	IntelliSys 2021 - Amsterdam, Netherlands Duration: 2 Sep 2021 → 3 Sep 2021

Publication series

Name	Lecture Notes in Networks and Systems
Publisher	Springer
Volume	294
ISSN (Print)	2367-3370

Conference

Conference	IntelliSys 2021
Country/Territory	Netherlands
City	Amsterdam
Period	2/09/21 → 3/09/21

Keywords

camera
wheelchair
disabled
one shot learning
Python
RCUK
EPSRC
EP/S005927/1

Access to Document

10.1007/978-3-030-82193-7_41

MH - 02 IntelliSys Pure
This is a post-peer-review, pre-copyedit version of an article by Haddad M., Sanders D., Langner M., Tewkesbury G. (2022) One Shot Learning Approach to Identify Drivers, published in Arai K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-82193-7_41.
Accepted author manuscript (Post-print), 286 KB

Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system
Sanders, D., Gegov, A. & Haddad, M.
Engineering and Physical Sciences Research Council
1/11/18 → 1/11/22
Project: Research

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One Shot Learning Approach to Identify Drivers
Malik Haddad (Speaker)
3 Sep 2021
Activity: Talk or presentation types › Oral presentation

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Cite this

@inproceedings{a8f471b10ad4457abce1d37870144b6f,

title = "One shot learning approach to identify drivers",

abstract = "This paper presents a new approach to identify users of shared powered mobility platfroms using One Shot learning algorithm. An electronic circuit is created using a Raspberry Pi and a camera module. Python programming language is used to create a program to control the function of the camera module and conduct One Shot learning to identify users. A user interface containing Start and Stop buttons is created to operate the camera module. If Start is pressed, the Python program will trigger the camera to take a snapshot, then the program will identify the face in that snapshot and perform facial recognition to compare that face with a database of user facial images. If a match was found a message box popped-up showing the user name. Once a user is identified, their settings and preferences can be downloaded to the shared powered mobility platform. If the face in the snapshot did not match any of the user facial images in the data base, a message popped-up showing that user was not found in the database. Practical testing showed the system behaved satisfactorily and successfully detected users. One Shot learning allowed new users to be added to the database without the need to retrain the whole system. ",

keywords = "camera, wheelchair, disabled, one shot learning, Python, RCUK, EPSRC, EP/S005927/1",

author = "Malik Haddad and David Sanders and Langner, {Martin Clifford} and Giles Tewkesbury",

year = "2021",

month = aug,

day = "4",

doi = "10.1007/978-3-030-82193-7_41",

language = "English",

isbn = "9783030821920",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "622--629",

editor = "K. Arai",

booktitle = "Proceedings of SAI Intelligent Systems Conference",

note = "IntelliSys 2021 ; Conference date: 02-09-2021 Through 03-09-2021",

}

Haddad, M, Sanders, D, Langner, MC & Tewkesbury, G 2021, One shot learning approach to identify drivers. in K Arai (ed.), Proceedings of SAI Intelligent Systems Conference: IntelliSys 2021: Intelligent Systems and Applications. Lecture Notes in Networks and Systems, vol. 294, Springer, pp. 622-629, IntelliSys 2021, Amsterdam, Netherlands, 2/09/21. https://doi.org/10.1007/978-3-030-82193-7_41

One shot learning approach to identify drivers. / Haddad, Malik; Sanders, David; Langner, Martin Clifford et al.

Proceedings of SAI Intelligent Systems Conference: IntelliSys 2021: Intelligent Systems and Applications. ed. / K. Arai. Springer, 2021. p. 622-629 (Lecture Notes in Networks and Systems; Vol. 294).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - One shot learning approach to identify drivers

AU - Haddad, Malik

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AU - Tewkesbury, Giles

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N2 - This paper presents a new approach to identify users of shared powered mobility platfroms using One Shot learning algorithm. An electronic circuit is created using a Raspberry Pi and a camera module. Python programming language is used to create a program to control the function of the camera module and conduct One Shot learning to identify users. A user interface containing Start and Stop buttons is created to operate the camera module. If Start is pressed, the Python program will trigger the camera to take a snapshot, then the program will identify the face in that snapshot and perform facial recognition to compare that face with a database of user facial images. If a match was found a message box popped-up showing the user name. Once a user is identified, their settings and preferences can be downloaded to the shared powered mobility platform. If the face in the snapshot did not match any of the user facial images in the data base, a message popped-up showing that user was not found in the database. Practical testing showed the system behaved satisfactorily and successfully detected users. One Shot learning allowed new users to be added to the database without the need to retrain the whole system.

AB - This paper presents a new approach to identify users of shared powered mobility platfroms using One Shot learning algorithm. An electronic circuit is created using a Raspberry Pi and a camera module. Python programming language is used to create a program to control the function of the camera module and conduct One Shot learning to identify users. A user interface containing Start and Stop buttons is created to operate the camera module. If Start is pressed, the Python program will trigger the camera to take a snapshot, then the program will identify the face in that snapshot and perform facial recognition to compare that face with a database of user facial images. If a match was found a message box popped-up showing the user name. Once a user is identified, their settings and preferences can be downloaded to the shared powered mobility platform. If the face in the snapshot did not match any of the user facial images in the data base, a message popped-up showing that user was not found in the database. Practical testing showed the system behaved satisfactorily and successfully detected users. One Shot learning allowed new users to be added to the database without the need to retrain the whole system.

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Y2 - 2 September 2021 through 3 September 2021

ER -

One shot learning approach to identify drivers

Abstract

Publication series

Conference

Keywords

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Projects

Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system

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One Shot Learning Approach to Identify Drivers

Cite this