The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 04, 2024

Filed:

Nov. 04, 2021
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Sushovan Chanda, Pune, IN;

Gauri Deshpande, Pune, IN;

Sachin Patel, Pune, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01); G06T 7/73 (2017.01); G06V 10/94 (2022.01); G06V 20/40 (2022.01); G06V 40/16 (2022.01); G06V 40/20 (2022.01);
U.S. Cl.
CPC ...
G06V 40/20 (2022.01); G06F 18/214 (2023.01); G06N 3/08 (2013.01); G06T 7/73 (2017.01); G06V 10/95 (2022.01); G06V 20/46 (2022.01); G06V 40/171 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30201 (2013.01);
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.


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