2015/2/10 Automating Tinder with Eigenfaces

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AUTOMATING TINDER WITH EIGENFACES ()

 (/)

While my friends were getting sucked into "swiping" all day on their phones

with Tinder, I eventually got fed up and designed a piece of software that

automates everything on Tinder.

2015/2/10 Automating Tinder with Eigenfaces

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Since Tinder’s rising popularity, including its use by Olympic athletes such as

snowboarder Rebecca Torr, Tinder has achieved critical adoption as a

launchpoint for singles meeting singles. Its rising popularity has encouraged

a wave of "Tinderbot" inventions by nerds doing things such as "swiping

right" for everyone near their location (and of course those pesky

spammers). It wasn't my intention to "one-up" the competition, but using

the facial recognition algorithm Eigenfaces I built a bot that learns when to

swipe right (like a person) AND swipe left (dislike a person) AND start your

conversations.

Dubbed "Tinderbox", the first version only took 3 weeks to build. It uses an

existing Tinder account and taps into Tinder APIs, which is nice so you don't

have to create an entirely new account. Tinderbox recreates the Tinder app

in your browser, including the inbox and discovery preferences. The

workflow is simple:

The built-in bot builds facial models using your likes/dislikes

Bot examines profile images, cropping faces

Faces are loaded into an "average" face representing choices

Eigenfaces are computed from average faces

Bot then makes future selections based on Eigenface comparison

Comparisons are essentially k-nearest neighbor selection

Eigenface (https://en.wikipedia.org/wiki/Eigenface) is a quick and easy

algorithm to implement facial recognition without the use of complex

software like OpenCV. Tinderbox first extracts faces using the Viola-Jones

framework - specifically the jViolaJones (https://github.com/tc/jviolajones)

implementation - and converts them to grayscale. Only pictures with single,

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identifiable faces are used (to filter out false positives). Then after each

image is normalized, its pixels are converted into a matrix where they are

then appended to a list of models. The models are then averaged into a

single face used for future comparison (as noted in the workflow above).

The bot requires you to make 60 yes/no choices before it has enough data

to choose on your behalf. Then its on full auto-pilot.

If you're interested in the code behind this, here's a snippet of the two main

defs for computing Eigenfaces:

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  /**   * Computes the EigenFaces matrix using a pixel matrix of multiple images.   * @param pixelMatrix   * @param meanColumn   */  def computeEigenFaces(pixelMatrix: Array[Array[Double]], meanColumn: Array[Double]): DoubleMatrix2D = {    val diffMatrix = MatrixHelpers.computeDifferenceMatrixPixels(pixelMatrix, meanColumn)    val covarianceMatrix = MatrixHelpers.computeCovarianceMatrix(pixelMatrix, diffMatrix)    val eigenVectors = MatrixHelpers.computeEigenVectors(covarianceMatrix)    computeEigenFaces(eigenVectors, diffMatrix)  }

  /**   * Computes the EigenFaces matrix for a dataset of Eigenvectors and a diff matrix.   * @param eigenVectors   * @param diffMatrix   */  def computeEigenFaces(eigenVectors: DoubleMatrix2D, diffMatrix: Array[Array[Double]]): DoubleMatrix2D = {    val pixelCount = diffMatrix.length    val imageCount = eigenVectors.columns()    val rank = eigenVectors.rows()    val eigenFaces = Array.ofDim[Double](pixelCount, rank)

    (0 to (rank‐1)).foreach { i =>      var sumSquare = 0.0      (0 to (pixelCount‐1)).foreach { j =>        (0 to (imageCount‐1)).foreach { k =>          eigenFaces(j)(i) += diffMatrix(j)(k) * eigenVectors.get(i,k)        }        sumSquare += eigenFaces(j)(i) * eigenFaces(j)(i)      }      var norm = Math.sqrt(sumSquare)      (0 to (pixelCount‐1)).foreach { j =>        eigenFaces(j)(i) /= norm      }    }    val eigenFacesMatrix = new DenseDoubleMatrix2D(pixelCount, rank)    eigenFacesMatrix.assign(eigenFaces)  }

And computing the distance is just as easy:

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  /**   * Computes the distance between two images.   * @param pixels1   * @param pixels2   */  private def computeImageDistance(pixels1: Array[Double], pixels2: Array[Double]): Double = {    var distance = 0.0    val pixelCount = pixels1.length    (0 to (pixelCount‐1)).foreach { i =>      var diff = pixels1(i) ‐ pixels2(i)      distance += diff * diff    }    Math.sqrt(distance / pixelCount)  }

If you're interested learning more about Eigenfaces, this paper

(https://dl.dropboxusercontent.com/u/37572555/Github/Face%20Recognition/FaceRecognition.pdf)

has a relatively good overview of the math/matrix operations as well as a

selection process using k-nearest neighbor. The paper is a description of

another Eigenface facial recognition system.

I also added a couple of features that are only in the paid version of Tinder.

"Undo" buttons are there in the event a wrong swipe was made or the bot

made a poor choice. Also, you can set your location to anywhere in the

world for travel or curiosity purposes.

The bot that runs in the background also has a messaging system that starts

conversations. Using StanfordNLP (http://nlp.stanford.edu/), the bot

analyzes the sentiment of each chat response and classifies it as positive or

negative. Using a "message tree" (see diagram below), the bot selects from

pre-programmed chat messages as a response based on the sender's

sentiment. This continues up to 3 replies until the user is notified that a chat

is ready to enter. The advantage of this? It removes the time involved in

filtering new Tinder matches since a lot of people tend to drop off and "go

dark" early in the process. Extended conversation is a strong indicator of

interest.

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What are the results so far? The bot is amazingly effective. I would estimate

an accuracy of up to 70% in its selections - though there may be a hindsight

bias. Using a brand new account, I did a quick test to see how quickly the

bot could get results. In 48 hours, the bot registered 21 matches (starting all

of those conversations), made 4 extended conversations, and the bot itself

made over 300 moves. A "move" is any step the bot makes in either sending

a message or making a swipe. And in that time I barely needed to touch the

app. I also created a dashboard to give me an overview of my metrics (see

screenshot below).

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What do girls think of the bot? I've gone on at least 10 dates with the help of

the bot and I've shown my partners the bot in its entirety. One date literally

didn't believe me and thought I was pulling her leg. Another person thought

it was really cool and wanted the full tour. All were in agreement that it is

not creepy, though some felt it was borderline. Kind of nice considering it's

not something you'd come across everyday.

Am I still using the bot? I've actually turned it off for now. Admittedly, it

worked too well and started to conflict with work. Although in a couple

cases I had follow-ups and I'm still seeing one person.

Check out the code online: https://github.com/crockpotveggies/tinderbox

(https://github.com/crockpotveggies/tinderbox). Please feel free to

contribute. I don't have current plans to pursue this commercially, though

the license allows personal use and modification.

(/2015/02/09/automating-tinder-with-eigenfaces.html)

Automating Tinder with EigenfacesWhile my friends were getting sucked into

"swiping" all day on their phones with

Tinder, I eventually got fed up and

designed a piece of software that

automates everything on Tinder.

Public Company; Our Humility

2015/2/10 Automating Tinder with Eigenfaces

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(/2014/11/01/public-company-public-humility.html)

Not nearly as big as a Facebook IPO, our

company, 3 Tier Logic, will be publicly

traded starting November 10. I'm about to

make a big change in my own life and I

have no idea how it will play out.

(/2014/10/30/amelia-earhart-metal-fragment-2-2-v-1.html)

Reopening Fragment 2-2-V-1, Amelia Earhart's Plane?(/2014/10/30/amelia-earhart-metal-fragment-2-2-v-1.html)

On October 29, (/2014/10/30/amelia-

earhart-metal-fragment-2-2-v-

1.html)TIGHAR gained attention

(www.history.com/news/researchers-

identify-fragment-of-amelia-earharts-

plane/?

cmpid=Social_Facebook_HITH_10292014_1)

speculating that growing evidence

suggests fragment 2-2-V-1 may belong to

Amelia Earhart's plane. However, this is far

from new, and has been refuted as far

back as 1992.

Finding Centrality, There's More to BetweennessBeing immersed in my own graph research

and development, I quickly ran into the

common problems with centrality in social

network analysis. Does it scale? Can it be

parallelized? Is it going to be effective in

my own problem solving?

2015/2/10 Automating Tinder with Eigenfaces

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CONTACT

(/2014/10/14/centrality-algorithms-betweenness-markov.html)

(/2014/08/25/review-ancient-jung-java.html)

Reviewing the (now) ancient Graph, JungAt 3 Tier Logic I've been building a

prototype for a future product behind the

scenes, and to demonstrate some of the

concepts I actually went with a Graph that

is now becoming a little outdated: Jung.

(/2014/07/06/tried-capture-bear-got-deer.html)

Got Deer, No Bear, But...(/2014/07/06/tried-capture-bear-got-deer.html)

In a follow up to my most

(/2014/07/06/tried-capture-bear-got-

deer.html) recent post

(http://crockpotveggies.com/2014/07/04/hunting-

for-bear-photography.html) I set out to see

if I could photograph bears out in the

British Columbia backcountry. The local

black bears didn't make an appearance,

but there's hope I still might see some typeof bear.

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(https://twitter.com/crockpotveggies)   (https://github.com/crockpotveggies)  

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