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| Content Provider | ACM Digital Library |
|---|---|
| Author | Mayberry, Addison Hu, Pan Tun, Yamin Marlin, Benjamin M. Ganesan, Deepak Salthouse, Christopher Smith-Freedman, Duncan |
| Abstract | The human eye offers a fascinating window into an individual's health, cognitive attention, and decision making, but we lack the ability to continually measure these parameters in the natural environment. The challenges lie in: a) handling the complexity of continuous high-rate sensing from a camera and processing the image stream to estimate eye parameters, and b) dealing with the wide variability in illumination conditions in the natural environment. This paper explores the power--robustness tradeoffs inherent in the design of a wearable eye tracker, and proposes a novel staged architecture that enables graceful adaptation across the spectrum of real-world illumination. We propose CIDER, a system that operates in a highly optimized low-power mode under indoor settings by using a fast Search-Refine controller to track the eye, but detects when the environment switches to more challenging outdoor sunlight and switches models to operate robustly under this condition. Our design is holistic and tackles a) power consumption in digitizing pixels, estimating pupillary parameters, and illuminating the eye via near-infrared, b) error in estimating pupil center and pupil dilation, and c) model training procedures that involve zero effort from a user. We demonstrate that CIDER can estimate pupil center with error less than two pixels $(0.6^{O}),$ and pupil diameter with error of one pixel (0.22mm). Our end-to-end results show that we can operate at power levels of roughly 7mW at a 4Hz eye tracking rate, or roughly 32mW at rates upwards of 250Hz. |
| Starting Page | 400 |
| Ending Page | 412 |
| Page Count | 13 |
| File Format | PDF EPUB |
| ISBN | 9781450336192 |
| DOI | 10.1145/2789168.2790096 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-09-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Pupil Near-infrared Power robustness tradeoff Neural network Wearable Eye tracking |
| Content Type | Text |
| Resource Type | Article |
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