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short-paper

A comprehensive analysis of Local Binary Convolutional Neural Network for fast face recognition in surveillance video

Authors:

Carolina Toledo Ferraz,

José Hiroki SaitoAuthors Info & Claims

WebMedia '18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web

Pages 265 - 268

https://doi.org/10.1145/3243082.3267444

Published: 16 October 2018 Publication History

Abstract

Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which has shown to provide significant reduction in the number of parameters to be learned at training. In this paper, we study the influence of network parameters in the scenario of face recognition, comparing LBCNN against other famous networks available in the literature in terms of sensibility and processing time. In our study, we also propose a pre-processing step on images to increase the accuracy of the model, besides investigating its behaviour with noisy images. Our experiments are carried on the Chokepoint dataset, whose face subimages were collected from video frames under real-world surveillance conditions, including variations in terms of illumination, sharpness, pose, and misalignment due to automatic face detection. The conclusion is that by using the Laplacian step and a reduced amount of LBC modules, it is possible to train LBCNN more quickly and with improved accuracy. In addition, it was found that LBCNN is very sensitive to noise and better results can be achieved when noisy images are inserted in the training set.

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Cited By

Shen LJin X(2023)VaBTFER: An Effective Variant Binary Transformer for Facial Expression RecognitionSensors10.3390/s2401014724:1(147)Online publication date: 27-Dec-2023
https://doi.org/10.3390/s24010147
Toledo Ferraz CBarcellos WPereira Junior OTrevisan Negri Borges TGarcia Manzato MGonzaga AHiroki Saito J(2021)A comparison among keyframe extraction techniques for CNN classification based on video periocular imagesMultimedia Tools and Applications10.1007/s11042-020-10384-9Online publication date: 13-Jan-2021
https://doi.org/10.1007/s11042-020-10384-9
Vijeikis RRaudonis VDervinis G(2021)Towards Automated Surveillance: A Review of Intelligent Video SurveillanceIntelligent Computing10.1007/978-3-030-80129-8_53(784-803)Online publication date: 6-Jul-2021
https://doi.org/10.1007/978-3-030-80129-8_53
Show More Cited By

Index Terms

A comprehensive analysis of Local Binary Convolutional Neural Network for fast face recognition in surveillance video
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification
        Object recognition
      2. Computer vision tasks
        Visual content-based indexing and retrieval

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Published In

cover image ACM Other conferences

WebMedia '18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web

October 2018

437 pages

ISBN:9781450358675

DOI:10.1145/3243082

General Chairs:
Manoel Carvalho Marques Neto
IFBA
,
Renato Lima Novais
IFBA
,
Carlos Ferraz
UFPE
,
Windson Viana
UFC

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SBC: Brazilian Computer Society
SIGMM: ACM Special Interest Group on Multimedia
CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
CGIBR: Comite Gestor da Internet no Brazil
CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

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Published: 16 October 2018

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WebMedia '18

WebMedia '18: Brazilian Symposium on Multimedia and the Web

October 16 - 19, 2018

BA, Salvador, Brazil

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WebMedia '18 Paper Acceptance Rate 37 of 111 submissions, 33%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Shen LJin X(2023)VaBTFER: An Effective Variant Binary Transformer for Facial Expression RecognitionSensors10.3390/s2401014724:1(147)Online publication date: 27-Dec-2023
https://doi.org/10.3390/s24010147
Toledo Ferraz CBarcellos WPereira Junior OTrevisan Negri Borges TGarcia Manzato MGonzaga AHiroki Saito J(2021)A comparison among keyframe extraction techniques for CNN classification based on video periocular imagesMultimedia Tools and Applications10.1007/s11042-020-10384-9Online publication date: 13-Jan-2021
https://doi.org/10.1007/s11042-020-10384-9
Vijeikis RRaudonis VDervinis G(2021)Towards Automated Surveillance: A Review of Intelligent Video SurveillanceIntelligent Computing10.1007/978-3-030-80129-8_53(784-803)Online publication date: 6-Jul-2021
https://doi.org/10.1007/978-3-030-80129-8_53
Casanova GYandun DGuerrero G(2020)Analysis of video surveillance images using computer vision in a controlled security environment2020 15th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI49556.2020.9141068(1-6)Online publication date: Jun-2020
https://doi.org/10.23919/CISTI49556.2020.9141068
K. Wickrama Arachchilage SIzquierdo E(2020)Deep-learned faces: a surveyEURASIP Journal on Image and Video Processing10.1186/s13640-020-00510-w2020:1Online publication date: 29-Jun-2020
https://doi.org/10.1186/s13640-020-00510-w

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