On 2020-01-13 11:34 p.m., Benson Muite wrote:

Speaking about howdy, I packaged it on COPR for testing purpose and looking for improvement.

Great, may be of interest:

https://github.com/boltgolt/howdy/issues/233

I will take a look. Note that I fork the repo for improving upstream codes and suggest them.
My initial worry is more on the security of the algorithms used in howdy and their effectiveness, rather than correct packaging and linux permissions. Internally Howdy uses convolutional neural networks (CNN - http://dlib.net/cnn_face_detector.py.html) and OpenCV to find and match faces. It would be nice if it had been subjected to stringent tests such as those done by NIST:

https://pages.nist.gov/frvt/html/frvt1N.html

howdy use Histogram of Oriented Gradients by default instead CNN according to its config.ini

https://github.com/boltgolt/howdy/blob/master/src/config.ini


see for example:

https://www.necam.com/AdvancedRecognitionSystems/NISTValidation/FingerprintFacial/

I am aware of fprintd but it is beyond my scope,

This is already packaged and has a wiki page:

https://koji.fedoraproject.org/koji/packageinfo?packageID=7228

https://fedoraproject.org/wiki/Features/Fingerprint

The source code of fprintd is at https://gitlab.freedesktop.org/libfprint/fprintd

For fingerprints, there also seem to be standards:

https://www.nist.gov/programs-projects/fingerprint-recognition

and a NIST implementation:

https://www.nist.gov/services-resources/software/nist-biometric-image-software-nbis

Not sure if fprintd matches these standards, or if there is something significantly better.


For biometric authentication applications such as fprintd and howdy, maybe some kind of quality assurances are required, in particular for hardware specifications and algorithm effectiveness, in addition to the normal packaging procedure.


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