Pose-aligned networks for deep attribute modeling
US9400925B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Feb 7, 2014 |
| Grant date | Jul 26, 2016 |
| Priority date | — |
| Expiry date | May 1, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/809
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Technology is disclosed for inferring human attributes from images of people. The attributes can include, for example, gender, age, hair, and/or clothing. The technology uses part-based models, e.g., Poselets, to locate multiple normalized part patches from an image. The normalized part patches are provided into trained convolutional neural networks to generate feature data. Each convolution neural network applies multiple stages of convolution operations to one part patch to generate a set of fully connected feature data. The feature data for all part patches are concatenated and then provided into multiple trained classifiers (e.g., linear support vector machines) to predict attributes of the image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.