Privacy Preserving Optics for Miniature Vision Sensors

Francesco Pittaluga, Sanjeev J. Koppal; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 314-324

Abstract


The next wave of micro and nano devices will create a world with trillions of small networked cameras. This will lead to increased concerns about privacy and security. Most privacy preserving algorithms for computer vision are applied after image/video data has been captured. We propose to use privacy preserving optics that filter or block sensitive information directly from the incident light-field before sensor measurements are made, adding a new layer of privacy. In addition to balancing the privacy and utility of the captured data, we address trade-offs unique to miniature vision sensors, such as achieving high-quality field-of-view and resolution within the constraints of mass and volume. Our privacy preserving optics enable applications such as depth sensing, full-body motion tracking, people counting, blob detection and privacy preserving face recognition. While we demonstrate applications on macro-scale devices (smartphones, webcams, etc.) our theory has impact for smaller devices.

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[bibtex]
@InProceedings{Pittaluga_2015_CVPR,
author = {Pittaluga, Francesco and Koppal, Sanjeev J.},
title = {Privacy Preserving Optics for Miniature Vision Sensors},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}