On 1/14/20 10:34 AM, Benson Muite wrote:
On 1/14/20 9:00 AM, Luya Tshimbalanga wrote:
>
> On 2020-01-13 12:56 a.m., Benson Muite wrote:
>>
>> On 1/12/20 9:38 AM, Luya Tshimbalanga wrote:
>>> The challenge about upstream is when they lack activity for years
>>> and contributions are very difficult when users lack knowledge of
>>> coding without proper guidance. For example, attempting to improve
>>> say CellWriter (sorely missing due to the lack of port to Wayland
>>> compositor) and howdy, a Windows Hello facial recognition like for
>>> convertible laptops turned out too much as a graphic designer and
>>> trying to get someone knowing to code turned out complex than
>>> anticipated.
>>>
>>> Only options is to actively test and give input so far.
>>>
>> Deepin Linux seems to have a face recognition login (or at least
>> support for this), but still searching for the implementation. The
>> two PAM based authentications (Howdy and PAM-facial-auth):
>>
>>
https://github.com/devinaconley/pam-facial-auth
>>
>>
https://github.com/boltgolt/howdy
>>
>> seem to suggest they are not intended when high security is
>> required. Tests on manufacturer developed authentication also seem
>> to suggest not so secure:
>>
>>
https://www.blackhat.com/presentations/bh-dc-09/Nguyen/BlackHat-DC-09-Ngu...
>>
>>
>> However, a number of banks and KFC do use this in China, so maybe a
>> good open source implementation is missing (something other than a
>> trial version). Most of these rely on machine learning algorithms,
>> maybe something machine learning SIG might be interested in.
>
> Thank you for the PDF. However, the presentation is sightly outdated
> given the listed hardware dating from 2008. Some modern laptops are
> equipped with a IR camera Windows Hello type device which could be
> suitable for iris recognition similar to devices like Samsung Galaxy S9.
Thanks for feedback. Not having to remember many passwords is very
useful.
Maybe am wrong about faces/fingerprints as passwords:
https://www.openwall.com/lists/oss-security/2019/05/08/5
>>
>> 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
>
> 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
>
> see for example:
>
>
https://www.necam.com/AdvancedRecognitionSystems/NISTValidation/Fingerpri...
>
>
>> 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-sof...
>
>
> 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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