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FERAtt: Facial Expression Recognition With Attention Net

Marrero Fernandez, Pedro D. and Guerrero Peña, Fidel A. and Ren, Tsang Ing and Cunha, Alexandre (2019) FERAtt: Facial Expression Recognition With Attention Net. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE , Piscataway, NJ, pp. 837-846. ISBN 9781728125060.

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We present a new end-to-end network architecture for facial expression recognition with an attention model. It focuses attention in the human face and uses a Gaussian space representation for expression recognition. We devise this architecture based on two fundamental complementary components: (1) facial image correction and attention and (2) facial expression representation and classification. The first component uses an encoder-decoder style network and a convolutional feature extractor that are pixel-wise multiplied to obtain a feature attention map. The second component is responsible for obtaining an embedded representation and classification of the facial expression. We propose a loss function that creates a Gaussian structure on the representation space. To demonstrate the proposed method, we create two larger and more comprehensive synthetic datasets using the traditional BU3DFE and CK+ facial datasets. We compared results with the PreActResNet18 baseline. Our experiments on these datasets have shown the superiority of our approach in recognizing facial expressions.

Item Type:Book Section
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URLURL TypeDescription
Cunha, Alexandre0000-0002-2541-6024
Additional Information:© 2019 IEEE. The authors thanks the financial support from the Brazilian funding agency FACEPE and CETENE for usage of the computational facility.
Funding AgencyGrant Number
Fundação do Amparo a Ciência e Tecnologia (FACEPE)UNSPECIFIED
Centro de Tecnologias Estratégicas do Nordeste (CETENE)UNSPECIFIED
Record Number:CaltechAUTHORS:20200417-134029548
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Official Citation:P. D. M. Fernandez, F. A. G. Peña, T. I. Ren and A. Cunha, "FERAtt: Facial Expression Recognition With Attention Net," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 837-846; doi: 10.1109/cvprw.2019.00112
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102609
Deposited By: Tony Diaz
Deposited On:17 Apr 2020 21:06
Last Modified:16 Nov 2021 18:13

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