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Joint Eye Tracking and Head Pose Estimation for Gaze Estimation. CS365 Project Guide - Prof. Amitabha Mukerjee. Ankan Bansal Salman Mohammad. Motivation. Human Computer Interaction Information about interest of the subject, e.g. advertisement research Analyze driver attention

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joint eye tracking and head pose estimation for gaze estimation

Joint Eye Tracking and Head Pose Estimation for Gaze Estimation

CS365 ProjectGuide - Prof. Amitabha Mukerjee

Ankan BansalSalman Mohammad

motivation
Motivation
  • Human Computer Interaction
  • Information about interest of the subject, e.g. advertisement research
  • Analyze driver attention
  • Device control by disabled people through eye position and head pose.
past work
Past Work
  • Eye locations and head pose have been separately used for gaze estimation
  • Eye location algorithms are sensitive to head pose
  • - Allow limited motion of head
  • Head pose estimation often requires multiple cameras
  • Head pose estimation alone fails to finely locate the gaze
approach
Approach
  • Appearancebased
  • Integration of head tracker and eye location estimation
  • Head pose defines the field of view
  • Eye locations adjust the gaze estimation
algorithm
Algorithm
  • Haar-like features for face detection
  • Viola Jones object detection framework
  • Classifier cascades
algorithm1
Algorithm
  • Isophotes – Curves connecting points of equal intensity
  • Eyes are characterized by radially symmetric brightness patterns
  • Find centres of the curved isophotes in the image
algorithm2
Algorithm
  • Cylindrical Head Model
  • Pose comprises of rotation parameters and translation parameters
  • Tracking is done using the Lucas Kanade Algorithm
  • Initial Eye locations used as reference points
  • An area is sampled around each reference point and eye detector is applied to these regions
  • These eye locations are used to validate the tracking process
algorithm3
Algorithm

Sampling Points on the Face for Tracking

algorithm4
Algorithm

Face Motion Tracking

algorithm5
Algorithm
  • Assumptions - Visual Field of View is defined by the head pose only
  • Point of interest is defined by the eyes
  • Visual Field of View can be approximated by a pyramid

Image from [1]

tools and data
Tools and Data
  • Live data from web camera
  • Eye API from [3]
  • Boston University Dataset [5]
work done
Work Done
  • Face Detection
  • Eye Locations
  • 2D - Face Tracking

Promised but Not Done

  • 3D Head Pose Estimation and Tracking
  • Visual Gaze Estimation
references
References
  • Roberto Valenti, NicuSebe, Theo Gevers: Combining Head Pose and Eye Location Information for Gaze Estimation. IEEE Transactions on Image Processing 21(2): 802-815 (2012)
  • Jing Xiao, Takeo Kanade, Jeffrey F. Cohn: Robust Full-Motion Recovery of Head by Dynamic Templates and Re-Registration Techniques. FGR 2002: 163-169
  • Roberto Valenti, Theo Gevers: Accurate eye center location and tracking using isophote curvature. CVPR 2008
  • Roberto Valenti, ZeynepYücel, Theo Gevers: Robustifying eye center localization by head pose cues. CVPR 2009: 612-618
  • http://csr.bu.edu/headtracking/uniform-light/
  • Jun-Su Jang, Takeo Kanade: Robust 3D Head Tracking by Online Feature Registration. IEEE International Conference on Automatic Face and Gesture Recognition, September, 2008.
  • Paul Viola, Michael Jones: Robust real-time object detection. IJCV, 2001