EmoP3D: A brain like pyramidal deep neural network for emotion recognition

Emanuel Di Nardo; Alfredo Petrosino; Ihsan Ullah

doi:10.1007/978-3-030-11015-4_46

EmoP3D: A brain like pyramidal deep neural network for emotion recognition

Emanuel Di Nardo
, Alfredo Petrosino
, Ihsan Ullah

Parthenope University of Naples

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

6 Citations (Scopus)

Abstract

The paper reports a new model based on the understanding and encompassing intelligence from brain i.e. biological pyramidal neurons, tailored for emotion recognition. Our objective is to introduce and utilize usage of non-Convolutional layers in models and show comparable or state-of-the-art performance for multi-class emotion recognition problem. We open-sourced the optimized code for researchers. Our model shows state-of-the-art performance on two emotion recognition datasets (eNTERFACE and Youtube) enhancing previous best result by $$9.47\%$$ and $$20.8\%$$, respectively.

Original language	English
Title of host publication	Computer Vision – ECCV 2018 Workshops, Proceedings
Editors	Laura Leal-Taixé, Stefan Roth
Publisher	Springer-Verlag
Pages	607-616
Number of pages	10
ISBN (Print)	9783030110147
DOIs	https://doi.org/10.1007/978-3-030-11015-4_46
Publication status	Published - 2019
Event	15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany Duration: 8 Sep 2018 → 14 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11131 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th European Conference on Computer Vision, ECCV 2018
Country/Territory	Germany
City	Munich
Period	8/09/18 → 14/09/18

Keywords

3DPyraNet
Convolutional neural network
Emotion recognition
Pyramidal neural network

Access to Document

10.1007/978-3-030-11015-4_46

Cite this

Di Nardo, E., Petrosino, A., & Ullah, I. (2019). EmoP3D: A brain like pyramidal deep neural network for emotion recognition. In L. Leal-Taixé, & S. Roth (Eds.), Computer Vision – ECCV 2018 Workshops, Proceedings (pp. 607-616). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11131 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-030-11015-4_46

Di Nardo, Emanuel ; Petrosino, Alfredo ; Ullah, Ihsan. / EmoP3D : A brain like pyramidal deep neural network for emotion recognition. Computer Vision – ECCV 2018 Workshops, Proceedings. editor / Laura Leal-Taixé ; Stefan Roth. Springer-Verlag, 2019. pp. 607-616 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "EmoP3D: A brain like pyramidal deep neural network for emotion recognition",

abstract = "The paper reports a new model based on the understanding and encompassing intelligence from brain i.e. biological pyramidal neurons, tailored for emotion recognition. Our objective is to introduce and utilize usage of non-Convolutional layers in models and show comparable or state-of-the-art performance for multi-class emotion recognition problem. We open-sourced the optimized code for researchers. Our model shows state-of-the-art performance on two emotion recognition datasets (eNTERFACE and Youtube) enhancing previous best result by \$\$9.47\textbackslash{}\%\$\$ and \$\$20.8\textbackslash{}\%\$\$, respectively.",

keywords = "3DPyraNet, Convolutional neural network, Emotion recognition, Pyramidal neural network",

author = "\{Di Nardo\}, Emanuel and Alfredo Petrosino and Ihsan Ullah",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",

year = "2019",

doi = "10.1007/978-3-030-11015-4\_46",

language = "English",

isbn = "9783030110147",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "607--616",

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booktitle = "Computer Vision – ECCV 2018 Workshops, Proceedings",

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Di Nardo, E, Petrosino, A & Ullah, I 2019, EmoP3D: A brain like pyramidal deep neural network for emotion recognition. in L Leal-Taixé & S Roth (eds), Computer Vision – ECCV 2018 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11131 LNCS, Springer-Verlag, pp. 607-616, 15th European Conference on Computer Vision, ECCV 2018, Munich, Germany, 8/09/18. https://doi.org/10.1007/978-3-030-11015-4_46

EmoP3D: A brain like pyramidal deep neural network for emotion recognition. / Di Nardo, Emanuel; Petrosino, Alfredo; Ullah, Ihsan.
Computer Vision – ECCV 2018 Workshops, Proceedings. ed. / Laura Leal-Taixé; Stefan Roth. Springer-Verlag, 2019. p. 607-616 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11131 LNCS).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - EmoP3D

T2 - 15th European Conference on Computer Vision, ECCV 2018

AU - Di Nardo, Emanuel

AU - Petrosino, Alfredo

AU - Ullah, Ihsan

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - The paper reports a new model based on the understanding and encompassing intelligence from brain i.e. biological pyramidal neurons, tailored for emotion recognition. Our objective is to introduce and utilize usage of non-Convolutional layers in models and show comparable or state-of-the-art performance for multi-class emotion recognition problem. We open-sourced the optimized code for researchers. Our model shows state-of-the-art performance on two emotion recognition datasets (eNTERFACE and Youtube) enhancing previous best result by $$9.47\%$$ and $$20.8\%$$, respectively.

AB - The paper reports a new model based on the understanding and encompassing intelligence from brain i.e. biological pyramidal neurons, tailored for emotion recognition. Our objective is to introduce and utilize usage of non-Convolutional layers in models and show comparable or state-of-the-art performance for multi-class emotion recognition problem. We open-sourced the optimized code for researchers. Our model shows state-of-the-art performance on two emotion recognition datasets (eNTERFACE and Youtube) enhancing previous best result by $$9.47\%$$ and $$20.8\%$$, respectively.

KW - 3DPyraNet

KW - Convolutional neural network

KW - Emotion recognition

KW - Pyramidal neural network

UR - https://www.scopus.com/pages/publications/85061703254

U2 - 10.1007/978-3-030-11015-4_46

DO - 10.1007/978-3-030-11015-4_46

M3 - Conference Publication

AN - SCOPUS:85061703254

SN - 9783030110147

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 607

EP - 616

BT - Computer Vision – ECCV 2018 Workshops, Proceedings

A2 - Leal-Taixé, Laura

A2 - Roth, Stefan

PB - Springer-Verlag

Y2 - 8 September 2018 through 14 September 2018

ER -

Di Nardo E, Petrosino A, Ullah I. EmoP3D: A brain like pyramidal deep neural network for emotion recognition. In Leal-Taixé L, Roth S, editors, Computer Vision – ECCV 2018 Workshops, Proceedings. Springer-Verlag. 2019. p. 607-616. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-11015-4_46

EmoP3D: A brain like pyramidal deep neural network for emotion recognition

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