Face recognition from unseen domains via learning of semantic features
US11947626B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 5, 2021 |
| Grant date | Apr 2, 2024 |
| Priority date | — |
| Expiry date | Oct 14, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/172
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for improving face recognition from unseen domains by learning semantically meaningful representations is presented. The method includes obtaining face images with associated identities from a plurality of datasets, randomly selecting two datasets of the plurality of datasets to train a model, sampling batch face images and their corresponding labels, sampling triplet samples including one anchor face image, a sample face image from a same identity, and a sample face image from a different identity than that of the one anchor face image, performing a forward pass by using the samples of the selected two datasets, finding representations of the face images by using a backbone convolutional neural network (CNN), generating covariances from the representations of the face images and the backbone CNN, the covariances made in different spaces by using positive pairs and negative pairs, and employing the covariances to compute a cross-domain similarity loss function.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.