Abstract
The last two years have witnessed the introduction and rapid expansion of products based upon large, systematically-gathered, street-level image collections, such as Google Street View, EveryScape, and Mapjack. In the process of gathering images of public spaces, these projects also capture license plates, faces, and other information considered sensitive from a privacy standpoint.
In this work, we present a system that addresses the challenge of automatically detecting and blurring faces and license plates for the purpose of privacy protection in Google
Street View. Though some in the field would claim face detection is “solved”, we show that state-of-the-art face detectors alone are not sufficient to achieve the recall desired for large-scale privacy protection. In this paper we present a system that combines a standard sliding-window detector tuned for a high recall, low-precision operating point with a fast post-processing stage that is able to remove additional false positives by incorporating domain-specific information not available to the sliding-window detector. Using a completely automatic system, we are able to sufficiently blur more than 89% of faces and 94 96% of license plates in evaluation sets sampled from Google Street View imagery.
1. Introduction
In the last two years, there has been a rapid expansion of systematically-gathered street-level imagery available on the web. The largest and probably most well-known collection to date is Google Street View1 [13]. Street View launched as part of Google Maps in May 2007 and has expanded rapidly since, at last count providing imagery from twelve countries on four continents. Other smaller products have found their niches around the world, including Map-
1http://maps.google.com/help/maps/streetview
jack2, Everyscape3, and Daum’s Road View4. What makes
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