Tag: facebook

Facial Recognition Challenge: Chad Smith & Will Ferrell

Facial Recognition Challenge: Chad Smith & Will Ferrell

The below summarizes Part 4 of a medium.com series by Adam Geitgey.
Check out the original articles: Part 1Part 2Part 3Part 4Part 5Part 6Part 7 and Part 8!

Adam Geitgey likes to write about computers and machine learning. He explains machine learning as “generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.” (Part 1)


Adam’s visual explanation of two machine learning applications (original from Part 1)

In the fourth part of his series on machine learning Adam touches on Facial Recognition. Facebook is one of the companies using such algorithms in real-time, allowing them to recognize your friends’ faces after you’ve tagged them only a few times. Facebook reports they recognize faces with 97% accuracy, which is comparable to our own, human facial recognition abilities!

Facebook’s algorithms recognizing and automatically tagging Adam’s family. Helpful or creepy? (original from Part 4)


Adam decided to put up a challenge: would a facial recognition algorithm be able to distinguish Will Ferrell (famous actor) from Chad Smith (famous rock musician)? Indeed, these two celebrities look very much alike:

Image result for will ferrell chad smith
Chad Smith (left) and Will Ferell (right) on www.rollingstone.com

If you want to train such an algorithm, Adam explain, you need to overcome a series of related problems:

  1. First, look at a picture and find all the faces in it
  2. Second, focus on each face and be able to understand that even if a face is turned in a weird direction or in bad lighting, it is still the same person.
  3. Third, be able to pick out unique features of the face that you can use to tell it apart from other people— like how big the eyes are, how long the face is, etc.
  4. Finally, compare the unique features of that face to all the people you already know to determine the person’s name.

(Adam Geitgey, Part 4)


How the facial recognition algorithm steps might work (original from Part 4)

To detect the faces, Adam used Histograms of Oriented Gradients (HOG). All input pictures were converted to black and white (because color is not needed) and then every single pixel in our image is examined, one at a time. Moreover, for every pixel, the algorithm examined the pixels directly surrounding it:

Illustration of the algorithm as it would take in a black and white photo of Will Ferrel (original from Part 4)

The algorithm then checks, for every pixel, in which direction the picture is getting darker and draws an arrow (a gradient) in that direction.

Illustration of how algorithm would reduce a black and white photo of Will Ferrel to gradients (original from Part 4)

However, to do this for every single pixel would require too much processing power, so Adam broke up pictures in 16 by 16 pixel squares. The result is a very simple representation that does capture the basic structure of the original face, based on which we can now spot faces in pictures. Moreover, because we used gradients, the result will be similar regardless of the lighting of the picture.

The original image turned into a HOG representation (original from Part 4)

Now that the computer can spot faces, we need to make sure that it knows that two perspectives of the same face represent the same person. Adam uses landmarks for this: 68 specific points that exist on every face. An algorithm can then be trained to find these points on any face:

The 68 points on the image of Will Ferrell (original from Part 4)

Now the computer knows where the chin, the mouth and the eyes are, the image can be scaled and rotated to center it as best as possible:

The image of Will Ferrell transformed (original from Part 4)

Adam trained a Deep Convolutional Neural Network to generate 128 measurements for each face that best distinguish it from faces of other people. This network needs to train for several hours, going through thousands and thousands of face pictures. If you want to try this step yourself, Adam explains how to run OpenFace’s lua script. This study at Google provides more details, but it basically looks like this:

The training process visualized (original from Part 4)

After hours of training, the neural net will output 128 numbers accurately representing the specific face put in. Now, all you need to do is check which face in your database is most closely resembled by those 128 numbers, and you have your match! Many algorithms can do this final check, and Adam trained a simple linear SVM classifier on twenty pictures of Chad Smith, Will Ferrel, and Jimmy Falon (the host of a talkshow they both visited).

In the end, Adam’s machine had learned to distinguish these three people – two of whom are nearly indistinguishable with the human eye – in real-time:

Adam Geitgey’s facial recognition algorithm in action: providing real time classifications of the faces of lookalikes Chad Smith and Will Ferrel at Jimmy Falon’s talk show (original from Part 4

You can find Adam on LinkedIn, or on Twitter at @ageitgey, and I strongly recommend you examine his series on machine learning on Medium.com (Part 1). Moreover, Adam released a Python library called face_recognition, arguably easier to install and use than OpenFace, as well as a pre-configured virtual machine with face_recognition, OpenCV, TensorFlow and lots of other deep learning tools pre-installed.


Predict the Sentimental Response to your Facebook Posts

Predict the Sentimental Response to your Facebook Posts

Max Woolf writes machine learning blogs on his personal blog, minimaxir, and posts open-source code repositories on his GitHub. He is a former Apple Software QA Engineer and graduated from Carnegie Mellon University. I have published his work before, for instance, this short ggplot2 tutorial by MiniMaxir, but his new project really amazed me.

Max developed a Facebook web scaper in Python. This tool gathers all the posts and comments of Facebook Pages (or Open Facebook Groups) and the related metadata, including post message, post links, and counts of each reaction on the post. The data is then exported to a CSV file, which can be imported into any data analysis program like Excel, or R.

The data format returned by the Facebook scaper.

Max put his scraper to work and gathered a ton of publicly available Facebook posts and their metadata between 2016 and 2017.

Responses to collected Facebook posts.

However, this was only the beginning. In a follow-up project, Max trained a recurrent neural network (or RNN) on these 2016-2017 data in order to predict the proportionate reactions (love, wow, haha, sad, angry) to any given text. Now, he has made this neural network publicly available with the Python 2/3 module and R package, reactionrnn, which builds on Keras/TensorFlow (see Keras: Deep Learning in R or Python within 30 seconds & R learning: Neural Networks).


reactionrnn architecture

Python implementation

For Python, reactionrnn can be installed from pypi via pip:

python3 -m pip install reactionrnn

You may need to create a venv (python3 -m venv <path>) first.

from reactionrnn import reactionrnn

react = reactionrnn()
react.predict("Happy Mother's Day from the Chicago Cubs!")
[('love', 0.9765), ('wow', 0.0235), ('haha', 0.0), ('sad', 0.0), ('angry', 0.0)]

R implementation

For R, you can install reactionrnn from this GitHub repo with devtools (working on resolving issues to get package on CRAN):

# install.packages('devtools')
devtools::install_github("minimaxir/reactionrnn", subdir="R-package")
react <- reactionrnn()
react %>% predict("Happy Mother's Day from the Chicago Cubs!")
      love        wow       haha        sad      angry 
0.97649449 0.02350551 0.00000000 0.00000000 0.00000000 

You can view a demo of common features in this Jupyter Notebook for Python, and this R Notebook for R.


  • reactionrnn is trained on Facebook posts of 2016 and 2017 and will often yield responses that are characteristic for this corpus.
  • reactionrnn will only use the first 140 characters of any given text.
  • Max intends to build a web-based implementation using Keras.js
  • Max also intends to improve the network (longer character sequences and better performance) and released it as a commercial product if any venture capitalists are interested.
  • Max’s projects are open-source and supported by his Patreon, any monetary contributions are appreciated and will be put to good creative use.