Amazon Pushes Facial Recognition to Police, Critics See Surveillance Risk
I find this article a bit funny, and see it mostly as hype.<p>I worked very revently on a large internal face detection service for a legal and compliance application at a large US company.<p>Being pragmatic engineers, the first thing we did was to pilot test Rekognition against an in-house prototype built by modifying some open source deep learning approaches.<p>Rekognition performed so poorly compared to our prototype (which was not state of the art or anything, just good enough for our company’s bespoke conpliance problem). It was just so bad. Overall intersection-over-union scores on our training &amp; acceptance data was really poor, and perhaps worse, the latency of the requests to Rekognition was abysmal and completely unusable for us, especially for images that had a large number of identifiable faces (Rekognition service calls isually timed out at 5 seconds and returned whatever amount of faces it could process).<p>Our very simple prototype could fully process upwards of 30 faces with 1 second of latency, and so if using a similar 5 second timeout limit, we could process around an order of magnitude more faces (which happens more often than you think, e.g. crowds, people in public places).<p>I just could not believe that the web service, backed by a team surely comprised of at least hundreds of top Amazon engineers, was so slow and inaccurate.<p>We didn’t even bother trying to price out how expensive it would be for our expected amount of throughput, because we knew from a simple accuracy and per-request latency point of view, Rekognition just couldn’t cut it.<p>As I’ve moved into a more lead role with machine learning teams, this experience really opened my eyes to how seductive cloud solutions can appear to non-technical managers.<p>They will act like severe bean counters when they see a high salary request from a really experienced machine learning engineer, but they won’t even bother with due diligence or estimating cost-per-request or cost-per-unit-accuracy for dropping money on a cloud service.<p>Instead of empowering a small team to do something much more cost-effective for the company, they’ll ball at their internal engineers while setting a pile of money ablaze.