Abstract
Automatic kinship verification from facial information is a relatively new and open research problem in computer vision. This paper explores the possibility of learning an efficient facial representation for video-based kinship verification by exploiting the visual transformation between facial appearance of kin pairs. To this end, a Siamese-like coupled convolutional encoder-decoder network is proposed. To reveal resemblance patterns of kinship while discarding the similarity patterns that can also be observed between people who do not have a kin relationship, a novel contrastive loss function is defined in the visual appearance space. For further optimization, the learned representation is fine-tuned using a feature-based contrastive loss. An expression matching procedure is employed in the model to minimize the negative influence of expression differences between kin pairs. Each kin video is analyzed by a sliding temporal window to leverage short-term facial dynamics. The effectiveness of the proposed method is assessed on seven different kin relationships using smile videos of kin pairs. On the average, 93:65% verification accuracy is achieved, improving the state of the art.
Original language | English |
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Title of host publication | 2017 IEEE International Conference on Computer Vision (ICCV) |
Editors | L. O'Conner |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 2478-2487 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-5386-1032-9 |
ISBN (Print) | 978-1-5386-1033-6 |
DOIs | |
Publication status | Published - 2017 |
Event | 2017 IEEE International Conference on Computer Vision (ICCV) - Venice, Italy Duration: 22 Oct 2017 → 29 Oct 2017 |
Conference
Conference | 2017 IEEE International Conference on Computer Vision (ICCV) |
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Abbreviated title | ICCV 2017 |
Country/Territory | Italy |
City | Venice |
Period | 22/10/17 → 29/10/17 |
Keywords
- Face
- Visualization
- Eyebrows
- Feature extraction
- Mouth
- Computer vision
- Optimization