Face Gesture Recognition Using Deep-Learning Models

Andrés Espinel; Noel Pérez; Daniel Riofrío; Diego Benítez; Ricardo Flores Moyano

doi:10.1109/ColCACI52978.2021.9469528

Face Gesture Recognition Using Deep-Learning Models

Andrés Espinel, Noel Pérez, Daniel Riofrío, Diego Benítez, Ricardo Flores Moyano

Universidad San Francisco de Quito

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

7 Citas (Scopus)

Resumen

This work compares face gesture recognition methods based on deep learning convolutional neural network (DCNN) architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing datasets. We validated the proposed architectures on three different databases: Jaffe, CK+, and the combination of both databases (Jaffe CK+) over a five-fold cross-validation strategy. The DCNN4, DCNN2, and DCNN+Autoencoder models achieved best performance mean accuracy scores of 95%, 94%, and 96% for the Jaffe, CK+, and Jaffe CK+ databases, respectively. Moreover, according to the cross-entropy loss function, the selected models did not incur overfitting.

Idioma original	Inglés
Título de la publicación alojada	2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings
Editores	Alvaro David Orjuela-Canon
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781665435345
DOI	https://doi.org/10.1109/ColCACI52978.2021.9469528
Estado	Publicada - 26 may. 2021
Evento	2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Virtual, Online, Colombia Duración: 26 may. 2021 → 28 may. 2021

Serie de la publicación

Nombre	2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings

Conferencia

Conferencia	2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021
País/Territorio	Colombia
Ciudad	Virtual, Online
Período	26/05/21 → 28/05/21

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10.1109/ColCACI52978.2021.9469528

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Espinel, A., Pérez, N., Riofrío, D., Benítez, D., & Moyano, R. F. (2021). Face Gesture Recognition Using Deep-Learning Models. En A. D. Orjuela-Canon (Ed.), 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings Artículo 9469528 (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ColCACI52978.2021.9469528

Espinel, Andrés ; Pérez, Noel ; Riofrío, Daniel et al. / Face Gesture Recognition Using Deep-Learning Models. 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings. editor / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings).

@inproceedings{8fbe7dbb14394ec883977b63e402ccf8,

title = "Face Gesture Recognition Using Deep-Learning Models",

abstract = "This work compares face gesture recognition methods based on deep learning convolutional neural network (DCNN) architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing datasets. We validated the proposed architectures on three different databases: Jaffe, CK+, and the combination of both databases (Jaffe CK+) over a five-fold cross-validation strategy. The DCNN4, DCNN2, and DCNN+Autoencoder models achieved best performance mean accuracy scores of 95%, 94%, and 96% for the Jaffe, CK+, and Jaffe CK+ databases, respectively. Moreover, according to the cross-entropy loss function, the selected models did not incur overfitting.",

keywords = "artificial intelligence, deep-learning models, face emotion recognition, face gesture classification, face images",

author = "Andr{\'e}s Espinel and Noel P{\'e}rez and Daniel Riofr{\'i}o and Diego Ben{\'i}tez and Moyano, {Ricardo Flores}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 ; Conference date: 26-05-2021 Through 28-05-2021",

year = "2021",

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Espinel, A, Pérez, N , Riofrío, D , Benítez, D & Moyano, RF 2021, Face Gesture Recognition Using Deep-Learning Models. En AD Orjuela-Canon (ed.), 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings., 9469528, 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021, Virtual, Online, Colombia, 26/05/21. https://doi.org/10.1109/ColCACI52978.2021.9469528

Face Gesture Recognition Using Deep-Learning Models. / Espinel, Andrés; Pérez, Noel ; Riofrío, Daniel et al.
2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings. ed. / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2021. 9469528 (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings).

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

TY - GEN

T1 - Face Gesture Recognition Using Deep-Learning Models

AU - Espinel, Andrés

AU - Pérez, Noel

AU - Riofrío, Daniel

AU - Benítez, Diego

AU - Moyano, Ricardo Flores

PY - 2021/5/26

Y1 - 2021/5/26

N2 - This work compares face gesture recognition methods based on deep learning convolutional neural network (DCNN) architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing datasets. We validated the proposed architectures on three different databases: Jaffe, CK+, and the combination of both databases (Jaffe CK+) over a five-fold cross-validation strategy. The DCNN4, DCNN2, and DCNN+Autoencoder models achieved best performance mean accuracy scores of 95%, 94%, and 96% for the Jaffe, CK+, and Jaffe CK+ databases, respectively. Moreover, according to the cross-entropy loss function, the selected models did not incur overfitting.

AB - This work compares face gesture recognition methods based on deep learning convolutional neural network (DCNN) architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing datasets. We validated the proposed architectures on three different databases: Jaffe, CK+, and the combination of both databases (Jaffe CK+) over a five-fold cross-validation strategy. The DCNN4, DCNN2, and DCNN+Autoencoder models achieved best performance mean accuracy scores of 95%, 94%, and 96% for the Jaffe, CK+, and Jaffe CK+ databases, respectively. Moreover, according to the cross-entropy loss function, the selected models did not incur overfitting.

KW - artificial intelligence

KW - deep-learning models

KW - face emotion recognition

KW - face gesture classification

KW - face images

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U2 - 10.1109/ColCACI52978.2021.9469528

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M3 - Contribución a la conferencia

AN - SCOPUS:85114212482

T3 - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings

BT - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings

A2 - Orjuela-Canon, Alvaro David

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021

Y2 - 26 May 2021 through 28 May 2021

ER -

Espinel A, Pérez N , Riofrío D , Benítez D , Moyano RF. Face Gesture Recognition Using Deep-Learning Models. En Orjuela-Canon AD, editor, 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. 9469528. (2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings). doi: 10.1109/ColCACI52978.2021.9469528

Face Gesture Recognition Using Deep-Learning Models

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