Unfortunately, its not a good averaging for many reasons I'll state below. So, I created my own, shown here:
I noticed a couple things about the Huffington Post image. It was a really cool idea, but it was extremely blurry so I investigated. It seems the website the creator got the images from has over 800 images for members of the house (I'll let you figure out what is wrong with that). It turns out it had a bunch of copies and black and white images mixed in. Also none of them were aligned. When averaging images, especially facial ones, it is always important to align them.
So I wrote a quick crawler in bash just to get the images from the wikipedia page for congress. Then I wrote some C++ code using OpenCV and my own modification of flandmark[1] to align the facial images. Once aligned, I then averaged them.
You can find the crawler code, the original and aligned images, and the alignment and averaging code on my Github at https://github.com/StevenHickson/AverageFaces. I used CMake, OpenCV, and Boost if you want to test it.
Feel free to use this image as long as you cite this page.
Update:
I was asked who was the closest and furthest away from the mean. The results are as follows:
Closest to the mean (left being the closest):
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| Stephen Fincher |
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| John Shimkus |
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| Xavier Becerra |
....
Furthest from the mean (left being the furthest):
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| Terri A. Sewell |
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| Corrine Brown |
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| Betty McCollum |
Consider donating to further my tinkering.
Places you can find me[1] M. Uricar, V. Franc and V. Hlavac, Detector of Facial Landmarks Learned by the Structured Output SVM, VISAPP '12: Proceedings of the 7th International Conference on Computer Vision Theory and Applications, 2012.
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