Mutual Information Maximization on Disentangled Representations for Differential Morph Detection
| Autor: | Nasser M. Nasrabadi, Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
021110 strategic defence & security studies Landmark business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Pattern recognition 02 engineering and technology Maximization Mutual information Image (mathematics) Domain (software engineering) Face (geometry) 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence Differential (infinitesimal) business |
| Zdroj: | WACV |
| Popis: | In this paper, we present a novel differential morph detection framework, utilizing landmark and appearance disentanglement. In our framework, the face image is represented in the embedding domain using two disentangled but complementary representations. The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image. This initially trained network is further trained for each dataset using contrastive representations. We demonstrate that, by employing appearance and landmark disentanglement, the proposed framework can provide state-of-the-art differential morph detection performance. This functionality is achieved by the using distances in landmark, appearance, and ID domains. The performance of the proposed framework is evaluated using three morph datasets generated with different methodologies. IEEE Winter Conference on Applications of Computer Vision (WACV 2021) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|