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Fast Face Detection in One Line of Code

This page will document some personal research into improving the performance of object detection algorithms on parallel and/or portable hardware.

For now that consists of the the titular paper and a very basic Android Demo of the algorithm.

 -  NotZed (notzed on google or internode e-mail)

Papers

First, I am not an academic and have not published any papers. None of these are peer-reviewed, edited, or even viewed by anyone by me at this time. If you're lucky i've probably remembered to run a spell-checker. In effect these are practice papers. I much prefer reading properly formatted and concise information and so have attempted to do the same. Although i'm not sure it's something I'd like to do every day, once in a while it has it's moments of satsifaction.

At this time I am not using any license and retain all rights - so distribution is not allowed under any circumstances. All publishing services (scribd, etc) are expressly forbidden from including copies.

But they are obviously easy enough to get right here: for humans to read.

On to the one paper so far: whether I do more will depend on whether I feel like it. Sponsorship and/or cool parallel hardware may change how i feel.

Links

Android Demo

Warning: This is very pre-alpha prototype code.

It has only been tested on a single tablet. Possibly the hit boxes will not show up in the right place and the screen might be flipped. It might crash. The device must have an ARM cpu that supports NEON and have a camera of course.

It displays a live view of the device's camera and overlays it with the raw hit-boxes of detected faces.

The slider adjusts the sensitivity. The button allows switching between the android face-detection api and the internal algorithm. The internal algorithm is about 70x faster on my tablet. All processing code is single-threaded.

While very accurate and surprisingly robust it fails badly on certain signals.

If it doesn't work properly: tough luck.

SOURCE CODE IS NOT AVAILABLE AT THIS TIME. Do not ask.

This page and all work is Copyright (C) 2013, 2014 Michael Zucchi, All Rights Reserved.