iShadow: Design of a wearable, real-time mobile gaze tracker

Presenter: Yamin Tun

Addison Mayberry, Pan Hu, Benjamin Marlin, Christopher Salthouse, Deepak Ganesan (UMass)

Introduction

Gaze Tracking Criteria Efficient (power, memory) Fast Accurate

Methodology: Hardware

Eye-facing camera

World-facing camera

Methodology: Hardware Architecture

Methodology: Hardware challenges

Methodology: Prediction Model

Artificial Neural Network (ANN):Sparse sampling

Learn best mask of a typical eye image

Predict Gaze from subsampled pixels

Methodology: Training ANN

Data Collection, Processing

10 subjects ~3000 images at 10 Hz fps (~5 mins)

Key Results~3 degree of error

Summary/Contribution

Low-power Real-time Gaze Tracker

Limitations/Critique Varying performance for different users

Different depth of view for different faces Visibility of the entire eye

Glasses placement Obstructing user’s view

Imager + motion sensor Gaze on same object with head movement