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Cited by 319

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Identity-Aware Convolutional Neural Network for Facial Expression Recognition

Meng, Zibo, Liu, Ping, Cai, Jie, Han, Shizhong, Tong, Yan·2017·2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)

Abstract

Facial expression recognition suffers under realworldconditions, especially on unseen subjects due to highinter-subject variations. To alleviate variations introduced bypersonal attributes and achieve better facial expression recognitionperformance, a novel identity-aware convolutional neuralnetwork (IACNN) is proposed. In particular, a CNN with a newarchitecture is employed as individual streams of a bi-streamidentity-aware network. An expression-sensitive contrastive lossis developed to measure the expression similarity to ensure thefeatures learned by the network are invariant to expressionvariations. More importantly, an identity-sensitive contrastiveloss is proposed to learn identity-related information from identitylabels to achieve identity-invariant expression recognition.Extensive experiments on three public databases including aspontaneous facial expression database have shown that theproposed IACNN achieves promising results in real world.

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Metadata

Authors
Meng, Z., Liu, P., Cai, J., Han, S., & Tong, Y.
Venue
2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
Publisher
IEEE
Published
2017-05
Pages
558-565
ISSN
1541-5058 (ISSN-L)
OpenAlex
W2730601341
Cited by
319
References
55
FWCI
36.17
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Identity-Aware Convolutional Neural Network for Facial Expression Recognition. https://app.cochat.ai/papers/10.1109/fg.2017.140
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Meng, Z., Liu, P., Cai, J., Han, S., & Tong, Y. (2017). Identity-Aware Convolutional Neural Network for Facial Expression Recognition. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). https://doi.org/10.1109/fg.2017.140

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