公开(公告)号：US12002291B2

公开(公告)日：2024-06-04

申请号：US17453634

申请日：2021-11-04

Applicant: Tata Consultancy Services Limited

Inventor： Sushovan Chanda ,  Gauri Deshpande ,  Sachin Patel

IPC: G06K9/00 ,  G06F18/214 ,  G06N3/08 ,  G06T7/73 ,  G06V10/94 ,  G06V20/40 ,  G06V40/16 ,  G06V40/20

CPC classification number: G06V40/20 ,  G06F18/214 ,  G06N3/08 ,  G06T7/73 ,  G06V10/95 ,  G06V20/46 ,  G06V40/171 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30201

Abstract: State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.

公开(公告)号：US20230018693A1

公开(公告)日：2023-01-19

申请号：US17453634

申请日：2021-11-04

Applicant: Tata Consultancy Services Limited

Inventor： Sushovan Chanda ,  Gauri Deshpande ,  Sachin Patel

IPC: G06K9/00 ,  G06T7/73 ,  G06K9/62 ,  G06N3/08

Abstract: State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.