What a Deep Neural Network Thinks About Your #selfie

Convolutional Neural Networks are great: they recognize things, places and people in your personal photos, signs, people and lights in self-driving cars, crops, forests and traffic in aerial imagery, various anomalies in medical images and all kinds of other useful things. But once in a while these powerful visual recognition models can also be warped for distraction, fun and amusement. In this fun experiment we're going to do just that: We'll take a powerful, 140-million-parameter state-of-the-art Convolutional Neural Network, feed it 2 million selfies from the internet, and train it to classify good selfies from bad ones. Just because it's easy and because we can. And in the process we might learn how to take better selfies :)

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To take a good selfie, Do:

• Be female. Women are consistently ranked higher than men. In particular, notice that there is not a single guy in the top 100.
• Face should occupy about 1/3 of the image. Notice that the position and pose of the face is quite consistent among the top images. The face always occupies about 1/3 of the image, is slightly tilted, and is positioned in the center and at the top. Which also brings me to:
• Cut off your forehead. What's up with that? It looks like a popular strategy, at least for women.
• Show your long hair. Notice the frequent prominence of long strands of hair running down the shoulders.
• Oversaturate the face. Notice the frequent occurrence of over-saturated lighting, which often makes the face look much more uniform and faded out. Related to that,
• Put a filter on it. Black and White photos seem to do quite well, and most of the top images seem to contain some kind of a filter that fades out the image and decreases the contrast.
• Add a border. You will notice a frequent appearance of horizontal/vertical white borders.

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