Method and system for confidence level detection from eye features

A system for confidence level detection, the method comprising, the system comprising:

• a memory storing instructions;
• one or more Input/Output—I/O interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• query a plurality of subjects with a predefined set of question;
• capture a plurality of video sequences corresponding to the plurality of subjects while the plurality of subjects respond to the predefined set of questions, wherein each video sequence among the plurality of video sequences captures a face of a corresponding subject from the plurality of subjects at predefined frames per second—FPS;
• extract a plurality of face features for each of the plurality of video sequences, wherein the plurality of face features comprise an Eye-Aspect Ratio—EAR a mouth movement, Horizontal Displacements—HDs, Vertical Displacements—VDs, Horizontal Squeezes—HSs and Vertical Peaks—VPs
• wherein the HDs computation for each of the video sequence comprises:
• identifying nose tip coordinates from the face in every frame of a video sequence among the plurality of video sequences;
• identifying eyebrow start coordinates and eyebrow end coordinates for eyebrows of the face in every frame with respect to the nose tip coordinates;
• and
• computing a plurality of HD values by determining a Euclidean distance between the eyebrow start coordinates and the eyebrow end coordinates of each of the eyebrows for a current frame and a previous frame of the video sequence, for all the frames in the video sequence;
• wherein the VDs computation for each of the video sequence comprises
• determining, for each of the eyebrows, a first highest point of a first eywbrow and a second highest point of a second eyebrow in vertical direction with respect to the identified nose tip coordinates in every frame;
• determining a highest vertical point for each frame by computing statistical mean of the first highest point and the second highest point;
• and
• computing a plurality of VD values by determining the Euclidean distance between the highest vertical point of the current frame and the previous frame of the video sequence, for all the frames in the video sequence;
• wherein the HSs computation for each of the video sequence comprises:
• identifying a HD threshold value by computing a statistical mean of the plurality of HD values;
• selecting a first subset of values from the plurality of HD values that are above the HD threshold value representing a plurality of HS values;
• and
• determining a horizontal count corresponding to number of the plurality of HS values appearing per second;
• wherein the VPs computation for each of the video sequence comprises:
• identifying a VD threshold value by computing a statistical mean of the plurality of VD;
• selecting a second subset of values from the plurality of VD values that are above the VD threshold value as a plurality of VP values;
• and
• determining a vertical count corresponding to number of the plurality of VP values appearing per second;
• and
• train a Neural Network—NN model, using the extracted plurality of face features from the plurality of video sequences to predict a confidence level of the subject as one among a plurality of confidence levels.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors causes a method for confidence level detection, the method comprising:

• querying a plurality of subjects with a predefined set of questions;
• capturing a plurality of video sequences corresponding to the plurality of subjects while the plurality of subjects respond to the predefined set of questions, wherein each video sequence among the plurality of video sequences captures a face of a corresponding subject from the plurality of subjects at predefined frames per second—FPS;
• extracting a plurality of face features for each of the plurality of video sequences, wherein the plurality of face features comprise an Eye-Aspect Ratio—EAR, a mouth movement, Horizontal Displacements—HD Vertical Displacements—VDs, Horizontal Squeezes—HSs and Vertical Peaks—VPs
• wherein the HDs computation for each of the video sequence comprises:
• identifying nose tip coordinates from the face in every frame of a video sequence among the plurality of video sequences;
• identifying eyebrow start coordinates and eyebrow end coordinates for eyebrows of the face in every frame with respect to the nose tip coordinates;
• and
• computing a plurality of HD values by determining a Euclidean distance between the eyebrow start coordinates and the eyebrow end coordinates of each of the eyebrows for a current frame and a previous frame of the video sequence, for all the frames in the video sequence;
• wherein the VDs computation for each of the video sequence comprises
• determining for each of the eyebrows, a first highest point of a first eyebrow and a second highest point of a second eyebrow in vertical direction with respect to the identified nose tip coordinates in every frame;
• determining a highest vertical point for each frame by computing statistical mean of the first highest point and the second highest point;
• and
• computing a plurality of VD values by determining the Euclidean distance between the highest vertical point of the current frame and the previous frame of the video sequence, for all the frames in the video sequence;
• wherein the HSs computation for each of the video sequence comprises:
• identifying a HD threshold value by computing a statistical mean of the plurality of HD values;
• selecting a first subset of values from the plurality of HD values that are above the HD threshold value representing a plurality of HS values;
• and
• determining a horizontal count corresponding to number of the plurality of HS values appearing per second;
• wherein the VPs computation for each of the video sequence comprises:
• identifying a VD threshold value by computing a statistical mean of the plurality of VD;
• selecting a second subset of values from the plurality of VD values that are above the VD threshold value as a plurality of VP values;
• and
• determining a vertical count corresponding to number of the plurality of VP values appearing per second;
• and
• training a Neural Network—NN model, using the extracted plurality of face features from the plurality of video sequences to predict a confidence level of the subject as one among a plurality of confidence levels.