公开(公告)号：US20180268207A1

公开(公告)日：2018-09-20

申请号：US15985173

申请日：2018-05-21

Applicant: Korea Institute of Science and Technology

Inventor： Ig Jae KIM ,  Heeseung CHOI ,  Sungyeon PARK ,  Junghyun CHO

IPC: G06K9/00 ,  G06K9/62 ,  G06T7/269

Abstract: A method for automatic facial impression transformation includes extracting landmark points for elements of a target face whose facial impression is to be transformed as well as distance vectors respectively representing distances of the landmark points, comparing the distance vectors to select a learning data set similar to the target face from a database, extracting landmark points and distance vectors from the learning data set, transforming a local feature of the target face based on the landmark points of the learning data set and score data for a facial impression, and transforming a global feature of the target face based on the distance vectors of the learning data set and the score data for the facial impression. Accordingly, a facial impression may be transformed in various ways while keeping an identity of a corresponding person.

公开(公告)号：US20170116705A1

公开(公告)日：2017-04-27

申请号：US15069095

申请日：2016-03-14

Applicant: Korea Institute of Science and Technology

Inventor： Ig Jae KIM ,  Heeseung CHOI ,  Sungyeon PARK ,  Junghyun CHO

IPC: G06T3/00 ,  G06K9/00

CPC classification number: G06T3/0093 ,  G06K9/00281 ,  G06K9/00302 ,  G06T11/00 ,  G06T15/00 ,  H04N7/147 ,  H04N7/157

Abstract: A method for automatic facial impression transformation includes extracting landmark points for elements of a target face whose facial impression is to be transformed as well as distance vectors respectively representing distances of the landmark points, comparing the distance vectors to select a learning data set similar to the target face from a database, extracting landmark points and distance vectors from the learning data set, transforming a local feature of the target face based on the landmark points of the learning data set and score data for a facial impression, and transforming a global feature of the target face based on the distance vectors of the learning data set and the score data for the facial impression. Accordingly, a facial impression may be transformed in various ways while keeping an identity of a corresponding person.