Abstract
Generating and recognizing facial expressions has numerous applications, however, those are limited by the scarcity of datasets containing labeled nuanced expressions. In this paper, we describe the use of Delaunay triangulation combined with simple morphing techniques to blend images of faces, which allows us to create and automatically label facial expressions portraying controllable intensities of emotion. We have applied this approach on the RafD dataset consisting of 67 participants and 8 categorical emotions and evaluated the augmentation in a facial expression generation and recognition tasks using deep learning models. For the generation task, we used a deconvolution neural network which learns to encode the input images in a high-dimensional feature space and generate realistic expressions at varying intensities. The augmentation significantly improves the quality of images compared to previous comparable experiments and it allows to create images with a higher resolution. For the recognition task, we evaluated pre-trained Densenet121 and Resnet50 networks with either the original or augmented dataset. Our results indicate that the augmentation alone has a similar or better performance compared to the original. Implications of this method and its role in improving existing facial expression generation and recognition approaches are discussed.
Original language | English |
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Title of host publication | Pattern Recognition ICPR International Workshops and Challenges |
Subtitle of host publication | Virtual Event, January 10–15, 2021, Proceedings, Part III |
Editors | Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani |
Place of Publication | Cham |
Publisher | Springer |
Pages | 730-740 |
Number of pages | 11 |
ISBN (Electronic) | 978-3-030-68796-0 |
ISBN (Print) | 978-3-030-68795-3 |
DOIs | |
Publication status | Published - 2021 |
Event | 25th International Conference on Pattern Recognition Workshops, ICPR 2020 - Milan, Italy Duration: 10 Jan 2021 → 15 Jan 2021 Conference number: 25 https://www.micc.unifi.it/icpr2020/ |
Publication series
Name | Lecture Notes in Computer Science (LNCS) |
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Volume | 12633 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Name | Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP) |
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Volume | 12663 |
Conference
Conference | 25th International Conference on Pattern Recognition Workshops, ICPR 2020 |
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Abbreviated title | ICPR 2020 |
Country/Territory | Italy |
City | Milan |
Period | 10/01/21 → 15/01/21 |
Internet address |
Keywords
- Augmentation
- Deep learning
- Emotions
- Facial expressions