Privacy preserving optics for miniature vision sensors

• Published in: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 314 - 324
• Main Authors: Pittaluga, Francesco, Koppal, Sanjeev J.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2015
• Subjects: Computer vision; Devices; Face; Miniature; Optical design; Optical imaging; Optical sensors; Pattern recognition; Preserving; Privacy; Sensors; Vision
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2015.7298628

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.