THESIS
2016
xi, 59 pages : illustrations ; 30 cm
Abstract
The growing popularity of mobile and wearable devices with builtin cameras, the bright prospect
of camera related applications such as augmented reality and lifelogging system, the increased ease
of taking and sharing photos, along with advances in computer vision techniques, have greatly facilitated
peoples lives in many aspects, but inevitably raised peoples concerns about visual privacy
at the same time.
Motivated by the finding that peoples privacy concerns are influenced by the context, in this
thesis, we propose Cardea, a context–aware and interactive visual privacy control framework that
enforces privacy policies according to peoples privacy preferences. The framework provides people
with finegrained visual privacy control using: i) personal privacy profiles, with which p...[
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The growing popularity of mobile and wearable devices with builtin cameras, the bright prospect
of camera related applications such as augmented reality and lifelogging system, the increased ease
of taking and sharing photos, along with advances in computer vision techniques, have greatly facilitated
peoples lives in many aspects, but inevitably raised peoples concerns about visual privacy
at the same time.
Motivated by the finding that peoples privacy concerns are influenced by the context, in this
thesis, we propose Cardea, a context–aware and interactive visual privacy control framework that
enforces privacy policies according to peoples privacy preferences. The framework provides people
with finegrained visual privacy control using: i) personal privacy profiles, with which people can
define their context–dependent privacy preferences; ii) natural visual indicators: face features, for
devices to automatically locate individuals who request privacy protection; iii) hand gestures, for
people to temporarily update and flexibly inform cameras of their privacy preferences. Benefited from recent progresses in face and object recognition, Cardea offers a way for context–dependent
privacy control in a natural and flexible manner, which differs from tag and marker based systems.
We design and implement the framework consisting of Android client app and cloud control server,
with convolutional neural networks as core of the image processing module. Our evaluation results
confirm such framework is practical and effective, showing promising future for context–aware
visual privacy control on mobile and wearable devices.
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