Conference proceeding
Deep Action Unit Classification using a Binned Intensity Loss and Semantic Context Model
2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), pp.4136-4141
International Conference on Pattern Recognition
01 Jan 2016
Abstract
One of the most important cues for human communication is the interpretation of facial expressions. We present a novel computer vision approach for Action Unit (AU) recognition based upon a deep learning framework combined with a semantic context model. We introduce a new convolutional neural network training loss specific to AU intensity that utilizes a binned cross entropy method to fine-tune an existing network. We demonstrate that this loss can be more effectively trained in comparison to an L2 regression or naive cross entropy approach. The results of our binned cross entropy neural network are then passed to our semantic model, which utilizes the co-occurrence of action units for improved binary and real valued classification. Through our qualitative and quantitative results, we demonstrate the improvement of our framework over the current state-of-the-art.
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Details
- Title
- Deep Action Unit Classification using a Binned Intensity Loss and Semantic Context Model
- Creators
- Edward Kim - Villanova UniversityShruthika Vangala - Villanova University
- Publication Details
- 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), pp.4136-4141
- Series
- International Conference on Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 6
- Grant note
- AWS in Education Grant award
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Identifiers
- 991021884691904721