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Deep multi-center learning for face alignment
- Source :
- Neurocomputing. 396:477-486
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Facial landmarks are highly correlated with each other since a certain landmark can be estimated by its neighboring landmarks. Most of the existing deep learning methods only use one fully-connected layer called shape prediction layer to estimate the locations of facial landmarks. In this paper, we propose a novel deep learning framework named Multi-Center Learning with multiple shape prediction layers for face alignment. In particular, each shape prediction layer emphasizes on the detection of a certain cluster of semantically relevant landmarks respectively. Challenging landmarks are focused firstly, and each cluster of landmarks is further optimized respectively. Moreover, to reduce the model complexity, we propose a model assembling method to integrate multiple shape prediction layers into one shape prediction layer. Extensive experiments demonstrate that our method is effective for handling complex occlusions and appearance variations with real-time performance. The code for our method is available at https://github.com/ZhiwenShao/MCNet-Extension.<br />This paper has been accepted by Neurocomputing
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Cognitive Neuroscience
Deep learning
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
02 engineering and technology
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
Face (geometry)
0202 electrical engineering, electronic engineering, information engineering
Code (cryptography)
020201 artificial intelligence & image processing
Center (algebra and category theory)
Artificial intelligence
Layer (object-oriented design)
business
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 396
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....ff0920ff2b861faadf720a8480f2239d