Identity-Aware Convolutional Neural Network for Facial Expression Recognition

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...

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Bibliographic Details
Published in2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) pp. 558 - 565
Main Authors Zibo Meng, Ping Liu, Jie Cai, Shizhong Han, Yan Tong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Face recognition
Feature extraction
Image recognition
Measurement
Spatiotemporal phenomena
Training
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Summary: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.
DOI:10.1109/FG.2017.140