Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/FG.2017.140 | 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) |
Keywords | Field | DocType |
identity-aware convolutional neural network,IACNN,facial expression recognition,unseen subjects,inter-subject variations,bistream identity-aware network,expression-sensitive contrastive loss,expression similarity measurement,feature learning,identity-sensitive contrastive loss,identity-related information learning,identity-invariant expression recognition,facial expression database | Facial expression recognition,Convolutional neural network,Computer science,Speech recognition,Facial expression,Artificial intelligence,Invariant (mathematics),Machine learning | Conference |
ISSN | ISBN | Citations |
2326-5396 | 978-1-5090-4024-7 | 32 |
PageRank | References | Authors |
1.04 | 47 | 5 |