Method and Apparatus for Eye Gaze Tracking and Detection of Fatigue

• US 20170119298A1
• Filed: 12/23/2016
• Published: 05/04/2017
• Est. Priority Date: 09/02/2014
• Status: Abandoned Application

• Current Assignee: Hong Kong Baptist University
• Original Assignee: Hong Kong Baptist University
• Inventors: CHEUNG, Yiu-ming
• Application Number:
  US15/389,519
• Publication Number:
  US 20170119298A1
• Time in Patent Office: Days
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: G02B 27/0093 with means for monitoring data relating to the user, e.g. head-tracking, eye-tracking
• CPC Class Codes: G06F 3/013 Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06T 1/20 Processor architectures; Processor configuration, e.g. pipelining
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20072 Graph-based image processing
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06V 40/165 using facial parts and geometric relationships
• CPC Class Codes: G06V 40/193 Preprocessing; Feature extraction

Abstract

An invention relates to method and apparatus of an eye gaze tracking system. In particular, the present invention relates to method and apparatus of an eye gaze tracking system using a generic camera under normal environment, featuring low cost and simple operation. The present invention also relates to method and apparatus of an accurate eye gaze tracking system that can tolerate large illumination changes. The present invention also presents a method and apparatus for detecting fatigue via the facial expressions of the user.

Claims

1. A user fatigue detection method implemented using at least one image capturing device and at least one computing processor, the method comprising the steps of:

localizing of the user'"'"'s face;

representing the user face and extracting image features therefrom;

aligning the user'"'"'s face and tracking the users'"'"' face; and

detecting the user fatigue.

2. The user fatigue detection method in accordance with claim 1, wherein the step of representing the user face and extracting image features comprises the step of:

using fast Histogram of Gradients to retrieve the features of an image.

3. The user fatigue detection method in accordance with claim 1, wherein the step of aligning the user'"'"'s face and tracking the users'"'"' face comprises the steps of:

using a Supervised Descent Model;

performing face alignment; and

performing face tracking.

4. The user fatigue detection method in accordance with claim 1, wherein the step of detecting the user fatigue comprises the steps of:

judging whether the user'"'"'s eyes are closed; and

judging whether the user'"'"'s head is bent.

5. The user fatigue detection method in accordance with claim 1, wherein model training is used.

6. The user fatigue detection method in accordance with claim 1, wherein multi-core acceleration is used.

7. A user fatigue detection apparatus comprising at least one image capturing device and at least one computing processor, the apparatus being configured to perform a process comprising the steps of:

localizing of the user'"'"'s face;

representing the user face and extracting image features therefrom;

aligning the user'"'"'s face and tracking the users'"'"' face; and

detecting the user fatigue.

8. The user fatigue detection apparatus in accordance with claim 7, wherein the step of representing the user face and extracting image features comprises the step of:

using fast Histogram of Gradients to retrieve the features of an image.

9. The user fatigue detection apparatus in accordance with claim 7, wherein the step of aligning the user'"'"'s face and tracking the users'"'"' face comprising the steps of:

using a Supervised Descent Model;

performing face alignment; and

performing face tracking.

10. The user fatigue detection apparatus in accordance with claim 7, wherein the step of detecting the user fatigue comprises the steps of:

judging whether the user'"'"'s eyes are closed; and

judging whether the user'"'"'s head is bent.

11. The user fatigue detection apparatus in accordance with claim 7, wherein model training is used.

12. The user fatigue detection apparatus in accordance with claim 7, wherein multi-core acceleration is used.