threshold/confidence about face_recognition HOT 16 CLOSED

Also when running the simple example, how can we print out the confidence of match?

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Question 1 : compare_faces is just a wrapper that call face_distance and applies a threshold. You can add an extra parameter to it to specify the threshold. For instance, for a value of 0.8 :

results = face_recognition.compare_faces([my_face_encoding], unknown_face_encoding,0.8)

Question 2 : If you need to see the confidence value, use the function face_distance directly : it will return a list of distances between the list of candidate faces and the target face.

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seems when i run basic example, i get an error about dlib. any thoughts on how to fix? i'm pretty sure i have it installed already:

import dlib
ImportError: No module named dlib

from face_recognition.

If you can't import dlib, then it's not installed (at least not for the version of Python you are running).

from face_recognition.

can your example be called on command line? Eg, for the basic example you gave, I would just run the below:

face_recognition known/ unknownpng/

How can I run it with compare_faces on command line?

from face_recognition.

Ah, good question. Unfortunately the command line application doesn't currently report confidence values. That's something I could add in the future, though.

from face_recognition.

so essentially I have to run your example as a script? something like below?

from PIL import Image
import face_recognition
results = face_recognition.compare_faces("Directory/known", "Directory/unknownjpg",0.8)

It seems not to like my input. Are [my_face_encoding], unknown_face_encoding meant to be directories or something else?

from face_recognition.

I think the confusion is that your original question was about the command-line application but the answer @sbourigault gave you was the answer if you are writing code using the python library instead. Those are two different things.

Unfortunately the command-line application doesn't currently return confidence values like you want. That's a new feature that would have to be added.

If you are familiar with Python coding and want to write your own script that acts like the command line application but returns those values, you could do that. But if you aren't, it might be easier to just wait until I have a chance to add that feature to the command-line application.

To answer your other question, the parameters to that function are not meant to be directories. The parameters expected by each function are listed in the API docs. There's also a minimal code example here that shows how it works.

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ok got it. Think I have things working now!

from face_recognition.

Awesome! Glad it's working.

from face_recognition.

Why do you recommend a threshold distance of point 6? Is that in literature somewhere? If so, where? Point 6 isn't a lot? It is 0 to 1 right?

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0.6 is what was most accurate with the training data set according to Davis King. See his post at http://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html

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"The network training started with randomly initialized weights and used a structured metric loss that tries to project all the identities into non-overlapping balls of radius 0.6. " So this means that lowering this to like point 2 or point 3 isn't going to give better results? Essentially he's somehow mapped things to point 6 if good fit? I'll have to read more papers later.

Separately, I also wish this code could sort the top matches. I ran it across a video and it returns sometimes several examples. It would be helpful to sort by confidence/distance and rank the results. But I don't think results are ranked in anyway.

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If instead of calling face_recognition.compare_faces(), you instead call face_recognition.face_distance(), that will return the distance between each possible match and the image you are testing. Then you could call scipy.stats.rankdata() to turn that into a ranked list (assuming you have scipy installed).

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@ageitgey Does this work well on any ethnicity? I'm trying to do some benchmarking on asian and chinese faces. I'm wondering how it will perform. Any idea if it will break down? My preliminary results on South American faces show several matches---I'm trying to reduce with confidence and understand, etc.

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@ageitgey Also, when you run the program, normally from command line, it does return matches with distance greater than .6. I know this, b/c when I take the matches and compare them separately with the distance program, to try to help rank them and understand, it shows the distance and for some matches they’re greater than point6, even though threshold was that. How is this possible?

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