eyeSelfie: Self Directed Eye Alignment using Reciprocal Eye Box Imaging
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eyeSelfie2015swedish.pdf
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Author(s)
Swedish, Tristan
Roesch, Karin
Lee, Ik-Hyun
Rastogi, Krishna
Bernstein, Shoshana
Raskar, Ramesh
Lee, Ik Hyun
Date Issued
August 2015
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Citation
Swedish, Tristan, Karin Roesch, Ik-Hyun Lee, Krishna Rastogi, Shoshana Bernstein, and Ramesh Raskar. "eyeSelfie: self directed eye alignment using reciprocal eye box imaging." ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2015, Volume 34 Issue 4, August 2015, Article No. 58.
Version
Author's final manuscript
Abstract
Eye alignment to the optical system is very critical in many modern devices, such as for biometrics, gaze tracking, head mounted displays, and health. We show alignment in the context of the most difficult challenge: retinal imaging. Alignment in retinal imaging, even conducted by a physician, is very challenging due to precise alignment requirements and lack of direct user eye gaze control. Self-imaging of the retina is nearly impossible.
We frame this problem as a user-interface (UI) challenge. We can create a better UI by controlling the eye box of a projected cue. Our key concept is to exploit the reciprocity, "If you see me, I see you", to develop near eye alignment displays. Two technical aspects are critical: a) tightness of the eye box and (b) the eye box discovery comfort. We demonstrate that previous pupil forming display architectures are not adequate to address alignment in depth. We then analyze two ray-based designs to determine efficacious fixation patterns. These ray based displays and a sequence of user steps allow lateral (x, y) and depth (z) wise alignment to deal with image centering and focus. We show a highly portable prototype and demonstrate the effectiveness through a user study.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2766970
