Real-time face identification from video surveillance cameras

Diego Acuña-Escobar; Julio Ibarra-Fiallo; Monserrate Intriago-Pazmiño

doi:10.1145/3368691.3368737

Real-time face identification from video surveillance cameras

Diego Acuña-Escobar, Julio Ibarra-Fiallo, Monserrate Intriago-Pazmiño

Matemáticas

Escuela Politecnica Nacional

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

In this research work, we present the implementation of a web environment and processing core based on convolutional neural networks, to recognize faces in real time. The environment can work with several video surveillance cameras at the same time. We conclude that ten images of a person are required for recognition to be highly reliable. The environment was tested with 32 volunteers, in 4 different environments: front face, smiling, wearing a cap, and glasses. We obtained 94%, 91%, and 90% in metrics true-positive-rate, precision, and accuracy, respectively.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019
Editorial	Association for Computing Machinery
ISBN (versión digital)	9781450372848
DOI	https://doi.org/10.1145/3368691.3368737
Estado	Publicada - 2 dic. 2019
Evento	2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 - Dubai, Emiratos Árabes Unidos Duración: 2 dic. 2019 → 5 dic. 2019

Serie de la publicación

Nombre	ACM International Conference Proceeding Series

Conferencia

Conferencia	2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019
País/Territorio	Emiratos Árabes Unidos
Ciudad	Dubai
Período	2/12/19 → 5/12/19

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10.1145/3368691.3368737

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Acuña-Escobar, D., Ibarra-Fiallo, J., & Intriago-Pazmiño, M. (2019). Real-time face identification from video surveillance cameras. En Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 Artículo a46 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3368691.3368737

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title = "Real-time face identification from video surveillance cameras",

abstract = "In this research work, we present the implementation of a web environment and processing core based on convolutional neural networks, to recognize faces in real time. The environment can work with several video surveillance cameras at the same time. We conclude that ten images of a person are required for recognition to be highly reliable. The environment was tested with 32 volunteers, in 4 different environments: front face, smiling, wearing a cap, and glasses. We obtained 94%, 91%, and 90% in metrics true-positive-rate, precision, and accuracy, respectively.",

keywords = "Convolutional neural network, Deep learning, Face recognition, People identification",

author = "Diego Acu{\~n}a-Escobar and Julio Ibarra-Fiallo and Monserrate Intriago-Pazmi{\~n}o",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery. ACM; 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 ; Conference date: 02-12-2019 Through 05-12-2019",

year = "2019",

month = dec,

day = "2",

doi = "10.1145/3368691.3368737",

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Acuña-Escobar, D, Ibarra-Fiallo, J & Intriago-Pazmiño, M 2019, Real-time face identification from video surveillance cameras. En Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019., a46, ACM International Conference Proceeding Series, Association for Computing Machinery, 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019, Dubai, Emiratos Árabes Unidos, 2/12/19. https://doi.org/10.1145/3368691.3368737

Real-time face identification from video surveillance cameras. / Acuña-Escobar, Diego; Ibarra-Fiallo, Julio; Intriago-Pazmiño, Monserrate.
Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019. Association for Computing Machinery, 2019. a46 (ACM International Conference Proceeding Series).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Real-time face identification from video surveillance cameras

AU - Acuña-Escobar, Diego

AU - Ibarra-Fiallo, Julio

AU - Intriago-Pazmiño, Monserrate

PY - 2019/12/2

Y1 - 2019/12/2

N2 - In this research work, we present the implementation of a web environment and processing core based on convolutional neural networks, to recognize faces in real time. The environment can work with several video surveillance cameras at the same time. We conclude that ten images of a person are required for recognition to be highly reliable. The environment was tested with 32 volunteers, in 4 different environments: front face, smiling, wearing a cap, and glasses. We obtained 94%, 91%, and 90% in metrics true-positive-rate, precision, and accuracy, respectively.

AB - In this research work, we present the implementation of a web environment and processing core based on convolutional neural networks, to recognize faces in real time. The environment can work with several video surveillance cameras at the same time. We conclude that ten images of a person are required for recognition to be highly reliable. The environment was tested with 32 volunteers, in 4 different environments: front face, smiling, wearing a cap, and glasses. We obtained 94%, 91%, and 90% in metrics true-positive-rate, precision, and accuracy, respectively.

KW - Convolutional neural network

KW - Deep learning

KW - Face recognition

KW - People identification

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M3 - Contribución a la conferencia

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T3 - ACM International Conference Proceeding Series

BT - Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019

PB - Association for Computing Machinery

T2 - 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019

Y2 - 2 December 2019 through 5 December 2019

ER -

Real-time face identification from video surveillance cameras

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