Shape Detection API Specification
This is the repository for
shape-detection-api, an experimental API for detecting Shapes (e.g. Faces, Barcodes, Text) in live or still images on the Web by using accelerated hardware/OS resources.
You're welcome to contribute! Let's make the Web rock our socks off!
Photos and images constitute the largest chunk of the Web, and many include recognisable features, such as human faces, text or QR codes. Detecting these features is computationally expensive, but would lead to interesting use cases e.g. face tagging or detection of high saliency areas. Users interacting with WebCams or other Video Capture Devices have become accustomed to camera-like features such as the ability to focus directly on human faces on the screen of their devices. This is particularly true in the case of mobile devices, where hardware manufacturers have long been supporting these features. Unfortunately, Web Apps do not yet have access to these hardware capabilities, which makes the use of computationally demanding libraries necessary.
QR/barcode/text detection can be used for:
- user identification/registration, e.g. for voting purposes;
- eCommerce, e.g. Walmart Pay;
- Augmented Reality overlay, e.g. here;
- Driving online-to-offline engagement, fighting fakes etc.
Face detection can be used for:
- producing fun effects, e.g. Snapchat Lenses;
- giving hints to encoders or auto focus routines;
- user name tagging;
- enhance accesibility by e.g. making objects appear larger as the user gets closer like HeadTrackr;
- speeding up Face Recognition by indicating the areas of the image where faces are present.
Current Related Efforts and Workarounds
Samsung Browser has a private API (click to unfold "Overview for Android", then search for "QR code reader").
TODO: compare a few JS/native libraries in terms of size and performance. A performance and detection comparison of some popular JS QR code scanners can be found here.
zxingjs2 has a list of some additional JS libraries.
Potential for misuse
Face Detection is an expensive operation due to the algorithmic complexity. Many requests, or demanding systems like a live stream feed with a certain frame rate, could slow down the whole system or greatly increase power consumption.
Platform specific implementation notes
What platforms support what detector?
Android provides both a stand alone software face detector and a interface to the hardware ones.
|FaceDetector||Software based using the Neven face detector||API Level 1, 2008|
|Vision.Face||Software based||Google Play services 7.2, Aug 2015|
|Camera2||Hardware||API Level 21/Lollipop, 2014|
|Camera.Face (old)||Hardware||API Level 14/Ice Cream Sandwich, 2011|
The availability of the actual hardware detection depends on the actual chip; according to the market share in 1H 2016 Qualcomm, MediaTek, Samsung and HiSilicon are the largest individual OEMs and they all have support for Face Detection (all the top-10 phones are covered as well):
- Qualcomm Snapdragon chipset family supports it since ~2013 as part of their ISP.
- MediaTek as part of CorePilot 2.0 (introduced in 2015).
- Samsung Exynos (at least 2013).
- Huawei HiSilicon Kirin950 since 2015 (this fabless manufacturer is relatively new).
- It is worth noting that ARM acquired Apical in 2016 for its computer vision expertise.
Mac OS X / iOS
Mac OS X/iOS provides
Vision Framework for Face, QR, Text and Rectangle detection in software or hardware.
|Vision Framework, Mac OS X||Software and Hardware||OS X v10.13, 2017|
|Vision Framework, iOS||Software and Hardware||IOS X v11.0, 2017|
|CIDetector, Mac OS X||Software||OS X v10.7, 2011|
|CIDetector, iOS||Software||iOS v5.0, 2011|
|AVFoundation||Hardware||iOS 6.0, 2012|
Apple has supported Face Detection in hardware since the Apple A5 processor introduced in 2011.
Examples and demos
Notes on bikeshedding
To compile, run:
curl https://api.csswg.org/bikeshed/ -F email@example.com -F force=1 > index.html
if the produced file has a strange size (i.e. zero), then something went terribly wrong; run instead
curl https://api.csswg.org/bikeshed/ -F firstname.lastname@example.org -F output=err
and try to figure out why
bikeshed did not like the