Learning deep face representation
US9400919B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 27, 2014 |
| Grant date | Jul 26, 2016 |
| Priority date | — |
| Expiry date | Jun 19, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/172
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy to implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. A very easy-to-implement deep learning framework for face representation is presented. The framework bases on pyramid convolutional neural network (CNN). The pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computation efficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.