Convolutional neural network super resolution for face recognition in surveillance monitoring

Pejman Rasti; Tõnis Uiboupin; Sergio Escalera; Gholamreza Anbarjafari

doi:10.1007/978-3-319-41778-3_18

Convolutional neural network super resolution for face recognition in surveillance monitoring

Pejman Rasti^*, Tõnis Uiboupin, Sergio Escalera, Gholamreza Anbarjafari

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

135 Citations (Scopus)

Abstract

Due to the importance of security in society, monitoring activities and recognizing specific people through surveillance video cameras play an important role. One of the main issues in such activity arises from the fact that cameras do not meet the resolution requirement for many face recognition algorithms. In order to solve this issue, in this paper we are proposing a new system which super resolves the image using deep learning convolutional network followed by the Hidden Markov Model and Singular Value Decomposition based face recognition. The proposed system has been tested on many well-known face databases such as FERET, HeadPose, and Essex University databases as well as our recently introduced iCV Face Recognition database (iCVF). The experimental results show that the recognition rate is improving considerably after apply the super resolution.

Original language	English
Title of host publication	Articulated Motion and Deformable Objects - 9th International Conference, AMDO 2016, Proceedings
Editors	Francisco José Perales, Josef Kittler
Number of pages	10
Publisher	Physica-Verlag
Publication date	2016
Pages	175-184
ISBN (Print)	9783319417776
DOIs	https://doi.org/10.1007/978-3-319-41778-3_18
Publication status	Published - 2016
Externally published	Yes
Event	9th International Conference on Articulated Motion and Deformable Objects, AMDO 2016 - Palma de Mallorca, Spain Duration: 13 Jul 2016 → 15 Jul 2016

Conference

Conference	9th International Conference on Articulated Motion and Deformable Objects, AMDO 2016
Country/Territory	Spain
City	Palma de Mallorca
Period	13/07/2016 → 15/07/2016
Sponsor	AERFAI (Spanish Association for Pattern Recognition and Image Analysis), Ajuntament de Palma, BMN, Banco Mare Nostrum, S.A, Escola Politecnica Superior (UIB) - Universitat de les Illes Balears (UIB), Mathematics and Computer Science Department, Universitat de les Illes Balears (UIB)

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9756
ISSN	0302-9743

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

Deep learning
Face recognition
Hidden markov model
Super resolution
Support vector machine
Surveillance videos

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10.1007/978-3-319-41778-3_18

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Rasti, P., Uiboupin, T., Escalera, S., & Anbarjafari, G. (2016). Convolutional neural network super resolution for face recognition in surveillance monitoring. In F. J. Perales, & J. Kittler (Eds.), Articulated Motion and Deformable Objects - 9th International Conference, AMDO 2016, Proceedings (pp. 175-184). Physica-Verlag. https://doi.org/10.1007/978-3-319-41778-3_18

Rasti, Pejman ; Uiboupin, Tõnis ; Escalera, Sergio et al. / Convolutional neural network super resolution for face recognition in surveillance monitoring. Articulated Motion and Deformable Objects - 9th International Conference, AMDO 2016, Proceedings. editor / Francisco José Perales ; Josef Kittler. Physica-Verlag, 2016. pp. 175-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9756).

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abstract = "Due to the importance of security in society, monitoring activities and recognizing specific people through surveillance video cameras play an important role. One of the main issues in such activity arises from the fact that cameras do not meet the resolution requirement for many face recognition algorithms. In order to solve this issue, in this paper we are proposing a new system which super resolves the image using deep learning convolutional network followed by the Hidden Markov Model and Singular Value Decomposition based face recognition. The proposed system has been tested on many well-known face databases such as FERET, HeadPose, and Essex University databases as well as our recently introduced iCV Face Recognition database (iCVF). The experimental results show that the recognition rate is improving considerably after apply the super resolution.",

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doi = "10.1007/978-3-319-41778-3_18",

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Rasti, P, Uiboupin, T, Escalera, S & Anbarjafari, G 2016, Convolutional neural network super resolution for face recognition in surveillance monitoring. in FJ Perales & J Kittler (eds), Articulated Motion and Deformable Objects - 9th International Conference, AMDO 2016, Proceedings. Physica-Verlag, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9756, pp. 175-184, 9th International Conference on Articulated Motion and Deformable Objects, AMDO 2016, Palma de Mallorca, Spain, 13/07/2016. https://doi.org/10.1007/978-3-319-41778-3_18

Convolutional neural network super resolution for face recognition in surveillance monitoring. / Rasti, Pejman; Uiboupin, Tõnis; Escalera, Sergio et al.
Articulated Motion and Deformable Objects - 9th International Conference, AMDO 2016, Proceedings. ed. / Francisco José Perales; Josef Kittler. Physica-Verlag, 2016. p. 175-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9756).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Convolutional neural network super resolution for face recognition in surveillance monitoring

AU - Rasti, Pejman

AU - Uiboupin, Tõnis

AU - Escalera, Sergio

AU - Anbarjafari, Gholamreza

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

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AB - Due to the importance of security in society, monitoring activities and recognizing specific people through surveillance video cameras play an important role. One of the main issues in such activity arises from the fact that cameras do not meet the resolution requirement for many face recognition algorithms. In order to solve this issue, in this paper we are proposing a new system which super resolves the image using deep learning convolutional network followed by the Hidden Markov Model and Singular Value Decomposition based face recognition. The proposed system has been tested on many well-known face databases such as FERET, HeadPose, and Essex University databases as well as our recently introduced iCV Face Recognition database (iCVF). The experimental results show that the recognition rate is improving considerably after apply the super resolution.

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ER -

Rasti P, Uiboupin T, Escalera S, Anbarjafari G. Convolutional neural network super resolution for face recognition in surveillance monitoring. In Perales FJ, Kittler J, editors, Articulated Motion and Deformable Objects - 9th International Conference, AMDO 2016, Proceedings. Physica-Verlag. 2016. p. 175-184. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9756). doi: 10.1007/978-3-319-41778-3_18

Convolutional neural network super resolution for face recognition in surveillance monitoring

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Keywords

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