公开(公告)号：US10789456B2

公开(公告)日：2020-09-29

申请号：US15856271

申请日：2017-12-28

Applicant: Adobe Inc.

Inventor： Yu Luo ,  Xin Lu ,  Jen-Chan Jeff Chien

IPC: G06K9/00 ,  G06N3/08 ,  G06K9/66 ,  G06N3/04 ,  G06N5/00 ,  G06N20/20 ,  G06N20/10

Abstract: Techniques are disclosed for a facial expression classification. In an embodiment, a multi-class classifier is trained using labelled training images, each training image including a facial expression. The trained classifier is then used to predict expressions for unlabelled video frames, whereby each frame is effectively labelled with a predicted expression. In addition, each predicted expression can be associated with a confidence score. Anchor frames can then be identified in the labelled video frames, based on the confidence scores of those frames (anchor frames are frames having a confidence score above an established threshold). Then, for each labelled video frame between two anchor frames, the predicted expression is refined or otherwise updated using interpolation, thereby providing a set of video frames having calibrated expression labels. These calibrated labelled video frames can then be used to further train the previously trained facial expression classifier, thereby providing a supplementally trained facial expression classifier.

公开(公告)号：US20190205625A1

公开(公告)日：2019-07-04

申请号：US15856271

申请日：2017-12-28

Applicant: Adobe Inc.

Inventor： Yu Luo ,  Xin Lu ,  Jen-Chan Jeff Chien

IPC: G06K9/00 ,  G06K9/66 ,  G06N3/08

Abstract: Techniques are disclosed for a facial expression classification. In an embodiment, a multi-class classifier is trained using labelled training images, each training image including a facial expression. The trained classifier is then used to predict expressions for unlabelled video frames, whereby each frame is effectively labelled with a predicted expression. In addition, each predicted expression can be associated with a confidence score. Anchor frames can then be identified in the labelled video frames, based on the confidence scores of those frames (anchor frames are frames having a confidence score above an established threshold). Then, for each labelled video frame between two anchor frames, the predicted expression is refined or otherwise updated using interpolation, thereby providing a set of video frames having calibrated expression labels. These calibrated labelled video frames can then be used to further train the previously trained facial expression classifier, thereby providing a supplementally trained facial expression classifier.