公开(公告)号：US20220284688A1

公开(公告)日：2022-09-08

申请号：US17685691

申请日：2022-03-03

Applicant: L'OREAL

Inventor： Zihao CHEN ,  Zhi Yu ,  Parham Aarabi

IPC: G06V10/24 ,  G06V10/82 ,  G06V10/70 ,  G06V10/776 ,  G06V40/16 ,  G06Q30/06

Abstract: With Convolutional Neural Networks (CNN), facial alignment networks (FAN) have achieved significant accuracy on a wide range of public datasets, which comes along with larger model size and expensive computation costs, making it infeasible to adapt them to real-time applications on edge devices. There is provided a model compression approach on FAN using One-Shot Neural Architecture Search to overcome this problem while preserving performance criteria. Methods and devices provide efficient training and searching (on a single GPU), and resultant models can deploy to run real-time in browser-based applications on edge devices including tablets and smartphones. The compressed models provide comparable cutting-edge accuracy, while having a 30 times smaller model size and can run 40.7 ms per frame in a popular browser on a popular smartphone and OS.