Patent application EP21201801A (EP), filed 2021-10-11
Publication number: EP4105827A1
Title: METHOD AND SYSTEM FOR PERSONALIZED EYE BLINK DETECTION
Abstract: Unlike state of art eye blink detection techniques that are generalized for usage across individuals affecting accuracy of eye blink prediction from subject to subject, embodiments of the present disclosure provide a method and system for personalized eye blink detection using passive camera-based approach. The method first generates a subject specific annotation data, which is then further processed to derive subject specific personalized blink threshold values. The method disclosed provides three unique approaches to compute the personalized blink threshold values which is one time calibration process. The personalized blink threshold values are then used to generate a binary decision vector (D) while analyzing input test images (video sequences) of the subject of interest. Further, values taken by elements of the decision vector (D) are analyzed for a predefined time period to predict possible eye blinks of the subject.
IPC codes: G06K   9/    62            A I
A61B   5/    11            A I
G06V  20/    59            A I
G06V  40/    18            A I
Claims: number: 1
text: A processor implemented method (200) for personalized eye blink detection, the method comprises:
number: 2
text: The method as claimed in claim 1, wherein the first blink threshold value is mathematically expressed as Bm + Bs, wherein Bm is the first average of the set of EAR values computed for each image among the blink tagged image set and Bs is the first standard deviation of the set of EAR values computed for each image among the blink tagged image set.
number: 3
text: The method as claimed in claim 1, wherein the second blink threshold value mathematically expressed as (t), wherein t = xcm ― k × (xcm ― xcmin), wherein xcm is the second average of each of the baseline corrected set of EAR values for each image among the plurality of images and xcmin is the minimum of each of the baseline corrected set of EAR values for each image among the plurality of images, and wherein k is an empirically derived constant.
number: 4
text: The method as claimed in claim 1, wherein the pair of threshold values is parameter of a conditional mathematical equation expressed as |xcMb ― yci| ≤ |xcMnb ― yci|, wherein xcMb is the second average of each of the base line corrected set of EAR values for each image from the blink tagged image set, and xcMnb is the second average of each of the base line corrected set of EAR values for each image from the no-blink tagged image set, and wherein the conditional mathematical equation is independent of a constant 'k'.
number: 5
text: A system (100) for personalized eye blink detection, the system (100) comprising:
number: 6
text: The system as claimed in claim 5, wherein the first blink threshold value is mathematically expressed as Bm + Bs, wherein Bm is the first average of the set of EAR values computed for each image among the blink tagged image set and Bs is the first standard deviation of the set of EAR values computed for each image among the blink tagged image set.
number: 7
text: The system as claimed in claim 5, wherein the second blink threshold value mathematically expressed as (t), wherein t = xcm ― k × (xcm ― xcmin), wherein xcm is the second average of each of the baseline corrected set of EAR values for each image among the plurality of images and xcmin is the minimum of each of the baseline corrected set of EAR values for each image among the plurality of images, and wherein k is an empirically derived constant.
number: 8
text: The system as claimed in claim 5, wherein the pair of threshold values are parameters of a conditional mathematical equation expressed as |xcMb ― yci| ≤ |xcMnb ― yci|, wherein xcMb is the second average of each of the base line corrected set of EAR values for each image from the blink tagged image set, and xcMnb is the second average of each of the base line corrected set of EAR values for each image from the no-blink tagged image set, and wherein the conditional mathematical equation is independent of a constant 'k'.
number: 9
text: 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 personalized eye blink detection, the method comprising:
Office action: search_report, dated 2022-12-21
Prior art cited: ref_id: - HUDA ET AL., MOBILE-BASED DRIVER SLEEPINESS DETECTION USING FACIAL LANDMARKS AND ANALYSIS OF EAR VALUES
ref_type: npl
severity: background
ref_id: EP3056375A1
ref_type: patent
severity: background
ref_id: - KUMAR ASHISH ET AL: "Driver drowsiness monitoring system using visual behaviour and machine learning", 2018 IEEE SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE), IEEE, 28 April 2018 (2018-04-28), pages 339 - 344, XP033372027, DOI: 10.1109/ISCAIE.2018.8405495
ref_type: npl
severity: background
ref_id: - HOSSAIN MD YOUSUF ET AL: "IOT Based Real-Time Drowsy Driving Detection System for the Prevention of Road Accidents", 2018 INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATICS AND BIOMEDICAL SCIENCES (ICIIBMS), IEEE, vol. 3, 21 October 2018 (2018-10-21), pages 190 - 195, XP033457420, DOI: 10.1109/ICIIBMS.2018.8550026
ref_type: npl
severity: background
ref_id: EP3056375
ref_type: patent
severity: background
ref_id: - KUMAR ASHISH ET AL, "Driver drowsiness monitoring system using visual behaviour and machine learning", 2018 IEEE SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE), IEEE, (20180428), doi:10.1109/ISCAIE.2018.8405495, pages 339 - 344, XP033372027 [I] 1-9 * the whole document *
ref_type: npl
severity: background
ref_id: - HOSSAIN MD YOUSUF ET AL, "IOT Based Real-Time Drowsy Driving Detection System for the Prevention of Road Accidents", 2018 INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATICS AND BIOMEDICAL SCIENCES (ICIIBMS), IEEE, (20181021), vol. 3, doi:10.1109/ICIIBMS.2018.8550026, pages 190 - 195, XP033457420 [A] 1-9 * p.4, section B to section C *
ref_type: npl
severity: background
Final disposition: pending (2022-12-21)