Key-point guided human attribute recognition using statistic correlation models
US11157727B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 27, 2017 |
| Grant date | Oct 26, 2021 |
| Priority date | — |
| Expiry date | Jan 28, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/103
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are provided for neural network based, human attribute recognition, guided by anatomical key-points and statistic correlation models. Attributes include characteristics that can be visibly identified or inferred from an image, such as gender, hairstyle, clothing style, etc. A methodology implementing the techniques according to an embodiment includes applying an attribute feature extraction (AFE) convolutional neural network (CNN) to an image of a human to generate attribute feature maps based on the image. The method further includes applying a key-point guided proposal (KPG) CNN to the image of the human to generate proposed hierarchical regions of the image based on associated anatomical key-points. The method further includes generating recognition probabilities for the human attributes using a CNN combination layer that incorporates the attribute feature maps, the proposed hierarchical regions, and statistical correlation models (SCMs) which provide correlations between the features of the attribute feature maps and the proposed hierarchical regions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.