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Last active December 13, 2023 12:00
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Before you start

Make sure you have python, OpenFace and dlib installed. You can either install them manually or use a preconfigured docker image that has everying already installed:

docker pull bamos/openface
docker run -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface

Pro-tip: If you are using Docker on OSX, you can make your OSX /Users/ folder visible inside a docker image like this:

docker run -v /Users:/host/Users -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface

Then you can access all your OSX files inside of the docker image at /host/Users/...

ls /host/Users/

Step 1

Make a folder called ./training-images/ inside the openface folder.

mkdir training-images

Step 2

Make a subfolder for each person you want to recognize. For example:

mkdir ./training-images/will-ferrell/
mkdir ./training-images/chad-smith/
mkdir ./training-images/jimmy-fallon/

Step 3

Copy all your images of each person into the correct sub-folders. Make sure only one face appears in each image. There's no need to crop the image around the face. OpenFace will do that automatically.

Step 4

Run the openface scripts from inside the openface root directory:

First, do pose detection and alignment:

./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96

This will create a new ./aligned-images/ subfolder with a cropped and aligned version of each of your test images.

Second, generate the representations from the aligned images:

./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/

After you run this, the ./generated-embeddings/ sub-folder will contain a csv file with the embeddings for each image.

Third, train your face detection model:

./demos/classifier.py train ./generated-embeddings/

This will generate a new file called ./generated-embeddings/classifier.pkl. This file has the SVM model you'll use to recognize new faces.

At this point, you should have a working face recognizer!

Step 5: Recognize faces!

Get a new picture with an unknown face. Pass it to the classifier script like this:

./demos/classifier.py infer ./generated-embeddings/classifier.pkl your_test_image.jpg

You should get a prediction that looks like this:

=== /test-images/will-ferrel-1.jpg ===
Predict will-ferrell with 0.73 confidence.

From here it's up to you to adapt the ./demos/classifier.py python script to work however you want.

Important notes:

  • If you get bad results, try adding a few more pictures of each person in Step 3 (especially picures in different poses).
  • This script will always make a prediction even if the face isn't one it knows. In a real application, you would look at the confidence score and throw away predictions with a low confidence since they are most likely wrong.
@poojashah89
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Can I use this on raspberry pi ?

@Simon-TheUser
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I was having the same issues with step 4. For me, it turns out that I accidentally copied my pictures in ./training-images/ instead of ./training-images/will-ferrell/.

@deepak2226
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./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/
I am trying to run this step but its prompting me require torch. I am using windows10. I tried alot to install torch on my windows machine but I am not getting the installation of torch for windows. Please advise

@subhadeeps
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I have used pre configured docker as mentioned in the blog.

I put a single face image in sub directory under training-images directory and run the command as mentioned in Step 3 and executed the command for Step 4.

Pose detection and alignment:

./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96
Output:
=== ./training-images/subhadeep/IMG_20180219_180131.jpg ===

Generate the representations from the aligned images:

./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/
Output:
{
data : "./aligned-images/"
imgDim : 96
model : "/root/openface/models/openface/nn4.small2.v1.t7"
device : 1
outDir : "./generated-embeddings/"
cache : false
cuda : false
batchSize : 50
}
./aligned-images/
cache lotation: /root/openface/aligned-images/cache.t7
Creating metadata for cache.
{
sampleSize :
{
1 : 3
2 : 96
3 : 96
}
split : 0
verbose : true
paths :
{
1 : "./aligned-images/"
}
samplingMode : "balanced"
loadSize :
{
1 : 3
2 : 96
3 : 96
}
}
running "find" on each class directory, and concatenate all those filenames into a single file containing all image paths for a given class
now combine all the files to a single large file
load the large concatenated list of sample paths to self.imagePath
1 samples found...... 0/1 ......................] ETA: 0ms | Step: 0ms
Updating classList and imageClass appropriately
[=================== 1/1 =====================>] Tot: 0ms | Step: 0ms
Cleaning up temporary files
Splitting training and test sets to a ratio of 0/100
nImgs: 1
Represent: 1/1

Later when I run the command for "train your face detection model", I have received an error:

./demos/classifier.py train ./generated-embeddings/
Output:
/root/.local/lib/python2.7/site-packages/sklearn/lda.py:4: DeprecationWarning: lda.LDA has been moved to discriminant_analysis.LinearDiscriminantAnalysis in 0.17 and will be removed in 0.19
"in 0.17 and will be removed in 0.19", DeprecationWarning)
Loading embeddings.
Training for 1 classes.
Traceback (most recent call last):
File "./demos/classifier.py", line 291, in
train(args)
File "./demos/classifier.py", line 166, in train
clf.fit(embeddings, labelsNum)
File "/root/.local/lib/python2.7/site-packages/sklearn/svm/base.py", line 151, in fit
y = self._validate_targets(y)
File "/root/.local/lib/python2.7/site-packages/sklearn/svm/base.py", line 521, in _validate_targets
% len(cls))
ValueError: The number of classes has to be greater than one; got 1

Can you help me how do I resolve the issue?

@sanketchobe
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Getting below error for training step in step 4.

Loading embeddings.
Training for 1 classes.
Traceback (most recent call last):
File "./demos/classifier.py", line 291, in
train(args)
File "./demos/classifier.py", line 166, in train
clf.fit(embeddings, labelsNum)
File "/root/.local/lib/python2.7/site-packages/sklearn/svm/base.py", line 151, in fit
y = self._validate_targets(y)
File "/root/.local/lib/python2.7/site-packages/sklearn/svm/base.py", line 521, in _validate_targets
% len(cls))
ValueError: The number of classes has to be greater than one; got 1

Anyone have any solution?

@Kabariya
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you have to have more than one folder in training-images folder and then before first step check is there any folder with name "aligned-images" if yes then get into that folder and remove catch file and then start from the first step. I was getting the same error i removed catch file and started from first step and now it's working :)

@toofo
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toofo commented Nov 20, 2018

Hello,

I am running code from this example . Everything works perfectly when I launch align of ./util/align-dlib.py from command line. However, when I am debugging "util/align-dlib" from Spyder I have:
it falls on line
import openface
With error
'No module named openface'

Is there some way to hardcode the reference to openface?

Thanks!

@tasjapr
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tasjapr commented Mar 1, 2019

Hi! I did everything according to the instructions, up to step 5 everything works. But when I go to step 5, I get an error:

Traceback (most recent call last):
File "./demos/classifier.py", line 298, in <module> infer(args, args.multi)
  File "./demos/classifier.py", line 196, in infer
      person = le.inverse_transform(maxI)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/label.py", line 273, in inverse_transform
    y = column_or_1d(y, warn=True)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 797, in column_or_1d
    raise ValueError("bad input shape {0}".format(shape))

ValueError: bad input shape ()

Solved by cmusatyalab/openface#393 (comment)

@aleksandra309303
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When executing lua batch-represent/main.lua -outDir generated-embeddings -data aligned-images I got :
table: 0x56399ed5d410
aligned-images
cache lotation: /mnt/c/Users/aleksandra/Desktop/Diplomski/openface/aligned-images/cache.t7
Creating metadata for cache.
table: 0x56399ee143d0
running "find" on each class directory, and concatenate all those filenames into a single file containing all image paths for a given class
now combine all the files to a single large file
load the large concatenated list of sample paths to self.imagePath
Segmentation fault
Did someone have this problem? Please, help

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