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Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation
- Source :
- CVPR
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- Facial action unit (AU) intensity estimation plays an important role in affective computing and human-computer interaction. Recent works have introduced deep neural networks for AU intensity estimation, but they require a large amount of intensity annotations. AU annotation needs strong domain expertise and it is expensive to construct a large database to learn deep models. We propose a novel knowledge-based semi-supervised deep convolutional neural network for AU intensity estimation with extremely limited AU annotations. Only the intensity annotations of peak and valley frames in training sequences are needed. To provide additional supervision for model learning, we exploit naturally existing constraints on AUs, including relative appearance similarity, temporal intensity ordering, facial symmetry, and contrastive appearance difference. Experimental evaluations are performed on two public benchmark databases. With around 2% of intensity annotations in FERA 2015 and around 1% in DISFA for training, our method can achieve comparable or even better performance than the state-of-the-art methods which use 100% of intensity annotations in the training set.
- Subjects :
- Similarity (geometry)
Training set
Artificial neural network
business.industry
Computer science
Pattern recognition
02 engineering and technology
Image segmentation
010501 environmental sciences
01 natural sciences
Convolutional neural network
Face (geometry)
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
020201 artificial intelligence & image processing
Artificial intelligence
business
Affective computing
Intensity (heat transfer)
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Accession number :
- edsair.doi...........a6bf3a9d92e04be367c3bcd66a83da74