Cover Image

Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera

Detection and prevention of criminal incidents using CCTV are currently increasing trend, for example, car and motorcycle parking lot. However, not continuous people monitoring and careless of events produce useless CCTV function for the prevention of criminal incidents. In this paper, face recognit...

Full description

Saved in:
Bibliographic Details
Main Authors: Halawa, Lavin J., Wibowo, A., Ernawan, Ferda
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2019
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/30064/1/08982383.pdf
http://umpir.ump.edu.my/id/eprint/30064/7/Face%20recognition%20using%20faster%20R-CNN.pdf
http://umpir.ump.edu.my/id/eprint/30064/
https://doi.org/10.1109/ICICoS48119.2019.8982383
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.30064
record_format eprints
spelling my.ump.umpir.300642023-12-15T04:10:44Z http://umpir.ump.edu.my/id/eprint/30064/ Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera Halawa, Lavin J. Wibowo, A. Ernawan, Ferda QA75 Electronic computers. Computer science Detection and prevention of criminal incidents using CCTV are currently increasing trend, for example, car and motorcycle parking lot. However, not continuous people monitoring and careless of events produce useless CCTV function for the prevention of criminal incidents. In this paper, face recognition is used for the recognition of vehicle owners in parking lots that are CCTV installed. The Faster-RCNN method is used for face detection and also for face recognition. Inception V2 architecture is utilized due to has a high accuracy among Convolutional Neural Network architecture. The best learning rate and epoch parameters for the Faster R-CNN model are optimized to improve face recognition on CCTV. In this research, the dataset consists of 6 people images with 50 faces images for each people, which used as training data, testing data, and validation data. IEEE 2019-10 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30064/1/08982383.pdf pdf en http://umpir.ump.edu.my/id/eprint/30064/7/Face%20recognition%20using%20faster%20R-CNN.pdf Halawa, Lavin J. and Wibowo, A. and Ernawan, Ferda (2019) Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera. In: 3rd International Conference on Informatics and Computational Sciences, ICICOS 2019 , 29 - 30 October 2019 , Semarang, Indonesia. pp. 1-6. (8982383). ISBN 978-172814610-2 https://doi.org/10.1109/ICICoS48119.2019.8982383
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Halawa, Lavin J.
Wibowo, A.
Ernawan, Ferda
Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera
description Detection and prevention of criminal incidents using CCTV are currently increasing trend, for example, car and motorcycle parking lot. However, not continuous people monitoring and careless of events produce useless CCTV function for the prevention of criminal incidents. In this paper, face recognition is used for the recognition of vehicle owners in parking lots that are CCTV installed. The Faster-RCNN method is used for face detection and also for face recognition. Inception V2 architecture is utilized due to has a high accuracy among Convolutional Neural Network architecture. The best learning rate and epoch parameters for the Faster R-CNN model are optimized to improve face recognition on CCTV. In this research, the dataset consists of 6 people images with 50 faces images for each people, which used as training data, testing data, and validation data.
format Conference or Workshop Item
author Halawa, Lavin J.
Wibowo, A.
Ernawan, Ferda
author_facet Halawa, Lavin J.
Wibowo, A.
Ernawan, Ferda
author_sort Halawa, Lavin J.
title Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera
title_short Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera
title_full Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera
title_fullStr Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera
title_full_unstemmed Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera
title_sort face recognition using faster r-cnn with inception-v2 architecture for cctv camera
publisher IEEE
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/30064/1/08982383.pdf
http://umpir.ump.edu.my/id/eprint/30064/7/Face%20recognition%20using%20faster%20R-CNN.pdf
http://umpir.ump.edu.my/id/eprint/30064/
https://doi.org/10.1109/ICICoS48119.2019.8982383
_version_ 1822923942668533760
score 13.232683

Similar Items