公开(公告)号：US20220198224A1

公开(公告)日：2022-06-23

申请号：US17510415

申请日：2021-10-26

Applicant: UBTECH ROBOTICS CORP LTD

Inventor： Hanliu Wang ,  Jun Cheng ,  Jianxin Pang

IPC: G06K9/62 ,  G06K9/00 ,  G06K9/32 ,  G06K9/20 ,  G06N3/04

Abstract: A backlight face recognition method, a terminal device using the same, and a computer readable storage medium are provided. The method includes: performing a face detection on each original face image in an original face image sample set to obtain a face frame corresponding to the original face image; capturing the corresponding original face images from the original face image sample set, and obtaining a new face image containing background pixels corresponding to the captured original face images from the original face image sample set; preprocessing all the obtained new face images to obtain a backlight sample set and a normal lighting sample set; and training a convolutional neural network using the backlight sample set and the normal lighting sample set until the convolutional neural network reaches a preset stopping condition. The trained convolutional neural network will improve the accuracy of face recognition in complex background and strong light.

公开(公告)号：US11709914B2

公开(公告)日：2023-07-25

申请号：US17510415

申请日：2021-10-26

Applicant: UBTECH ROBOTICS CORP LTD

Inventor： Hanliu Wang ,  Jun Cheng ,  Jianxin Pang

IPC: G06V40/16 ,  G06F18/214 ,  G06V10/141 ,  G06V10/94 ,  G06V10/24 ,  G06N3/04

CPC classification number: G06F18/2148 ,  G06N3/04 ,  G06V10/141 ,  G06V10/245 ,  G06V10/95 ,  G06V40/166 ,  G06V40/171

Abstract: A backlight face recognition method, a terminal device using the same, and a computer readable storage medium are provided. The method includes: performing a face detection on each original face image in an original face image sample set to obtain a face frame corresponding to the original face image; capturing the corresponding original face images from the original face image sample set, and obtaining a new face image containing background pixels corresponding to the captured original face images from the original face image sample set; preprocessing all the obtained new face images to obtain a backlight sample set and a normal lighting sample set; and training a convolutional neural network using the backlight sample set and the normal lighting sample set until the convolutional neural network reaches a preset stopping condition. The trained convolutional neural network will improve the accuracy of face recognition in complex background and strong light.