We used specifically named sizes to avoid that problem although whitelisting in the edge servers would also work.
I also used PIL a few years back to do image generation from text. I kept running into memory fragmentation bugs, weird artifacts creeping into images, etc. I haven't been able to recommend PIL for any serious work since then. Has this gotten better?
We use ImageMagick for cropping and resizing, PIL was just easier for the example. I haven't noticed any issue with PIL but I also haven't used it for any serious image processing.
I'm curious to know what the success rate of SeatGeek's process is.
Sports shots in our case don't get a great hit rate. Adjusting the `minNeighbors` parameter can help out with that depending on how many false positives you can accept. Musical artist misses are in the single digit percentages although shadows and strange backgrounds can give some additional faces that we don't really care for.
When collecting images we are now searching for those with more direct faces visible to make the detection easier. After that though we just try to get the face in the direct center and fall back to hoping the face is in that spot if we can't detect any.
At some point I want to try checking for partial face matches as well which should help in major sports since we tend not to use headshots.
I haven't touched the app in about a month, updated the library to the most recent version, and I'm now getting a 100% hit rate. I guess the library was a bit buggy.
Also, these headshots are the ones the players take before the season starts, not action shots. In theory there should be a high hit rate.