
Show HN: OpenFace – Face recognition with Google's FaceNet deep neural network - bdamos
https://github.com/cmusatyalab/openface
======
kevinsimper
Very interesting, i recently worked with OpenBiometric [1] and made a guide
how to use it very easy [2].

How different is OpenFace vs. OpenBR?

[1] [http://openbiometrics.org/](http://openbiometrics.org/)

[2] [https://github.com/kevinsimper/face-
recognition](https://github.com/kevinsimper/face-recognition)

~~~
bdamos
Summary: OpenFace uses fundamentally different techniques (a deep neural
network) for face recognition that OpenBR currently doesn't provide.

\--

As our initial ROC curve on LFW's similarity benchmark in
[https://github.com/cmusatyalab/openface/blob/master/images/n...](https://github.com/cmusatyalab/openface/blob/master/images/nn4.v1.lfw.roc.png)
shows, this approach results in slightly improved performance. The best point
is an FPR of 0.0 and TPR of 1.0 (top left). You can see today's state-of-the-
art private systems in the top left, followed by open source systems, then by
historical techniques OpenCV provides like Eigenfaces. The dashed line in the
middle shows what randomly guessing would provide.

OpenBR is going in a great direction for reproducible and open face
recognition. They provide a pipeline for preprocessing and representing faces,
as well as doing similarity and classification tasks on the representations.
The techniques from OpenFace could be integrated into OpenBR's pipeline.

~~~
kevinsimper
That sounds awesome, I don't quite understand it that about pipeline but maybe
in future! hehe

------
M4v3R
Would it help somehow if I provided the authors with some of my private photos
for training the network? I have thousands photos that have faces/names tagged
on them in my Photos app (OS X). If that would be of any help, I would happily
provide some of them.

~~~
bdamos
Thanks for the offer! Our original model `nn4.v1` should perform OK on your
data if you're interested in trying to automatically predict people in new
images.

Training new models is currently dominated by huge industry datasets, which
currently have 100's of millions of images. My current dataset is from
datasets available for research and has ~500k images.

------
what-no-tests
Without access to a crowdsourced database of faces-to-identities this library
is next to useless.

~~~
devit
Facebook?

~~~
spike021
Problem is not all photos (profile photos especially) are close-ups of a
person's face. Although they could use facial tagging and maybe come up with a
composite? I'm probably wrong though.

~~~
dogma1138
You don't need all photos, you can run a basic face recognition algorithm like
the ones used in cameras to identify good candidates and just filter all other
photos.

Profile photos are good enough, and with the amount of selfies people are
taking there should be more than enough candidates for a full facial
recognition matching.

Also facebook isn't the only social network, LinkedIn profile pictures are
usually much better for facial recognition Google+ profile pictures are also
usually quite good because they crop your face into that silly circle.

~~~
spike021
Oh I see. thanks for the explanation. LinkedIn definitely makes more sense too
since more users will have clearer profile shots.

