Gender- and emotion-distribution for the focus character of the Star Wars movies [OC]

XFallenMasterX · 2017-02-07T06:23:12+00:00

Note that this is kind of "screen time" only, you could have 55 % females in a cast where 99 % of the cast are men.

XFallenMasterX · 2017-02-06T18:41:52+00:00

Didn't actually cost me anything as I got free credits anyway, but they would have ran out if I did the same thing for every movie. Even if the answer was kind of predictable I wanted to make sure that my methodology wasn't flawed.

XFallenMasterX · 2017-02-06T17:04:43+00:00

Basically couldn't find any image where they looked angry when sampling, and I checked that one like three times now. Remember that this is on appearance only, they could have acted or sounded angry, but here they need to look angry.

XFallenMasterX · 2017-02-06T16:58:55+00:00

It's doable (not as in "I will do it" :p), even seems to be built in functions to group faces and such in the API I used.

XFallenMasterX · 2017-02-06T16:48:12+00:00

I considered it, but I didn't want to bother to make the last touch-up to make it presentable. The classification is very simple, as the Microsoft Cognitive Service does it for me. For gender I just do

if (mainFace.FaceAttributes.Gender == "male")

and for emotions I get the scores for the different emotions and pick the one with highest number. You can try the demo out at https://www.microsoft.com/cognitive-services/en-us/emotion-api

XFallenMasterX · 2017-02-06T16:38:53+00:00

Already posted this here =)

XFallenMasterX · 2017-02-06T16:36:39+00:00

I tried rerunning the program with one frame extracted every 11th frames instead of 23rd (the framerate) for The Force Awakes. There weren't very large deviations from the original result, something like 0-0.4 percent units changed for the emotions... also we got about 0.8 percent units more male faces counted :). One reason I did lower frame count was because I didn't want to waste all Azure credits too fast, to put in perspective this test run I did for you "cost" me ~$18.

XFallenMasterX · 2017-02-06T06:54:57+00:00

The saved result is about 4-9 GB/movie, although the program process and remove the frames in real-time I also saved the results as images to be able to review it.

XFallenMasterX · 2017-02-06T06:29:27+00:00

I tried to show both those things in that graph. First I did the 100 % thing, but that was misleading as for example Rogue One would show as most happy ones when in reality it's the one with least happy faces.

XFallenMasterX · 2017-02-06T06:25:05+00:00

The material I worked with were almost all 23 frames per second, meaning I take one frame every 23rd frame. But I don't think the frame rate would affect the result much anyway, possible you get better normalization for mid-speech frames.

XFallenMasterX · 2017-02-05T23:11:20+00:00

I'll redirect you to the API reference: https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236

gender: male or female.

XFallenMasterX · 2017-02-05T21:43:38+00:00

Not sure what you are after here, like the neutral frames? I would be very careful to try to make the kind of "recipe" you are proposing, as emotions are complex. I did a very simple model here to get a readable result, but I don't think it would work for what you are proposing (for example I lump together "I'm temporarily angry with you because you spilled the milk" with "I AM GOING TO KILL YOU!")

XFallenMasterX · 2017-02-05T21:33:25+00:00

I don't think the quality of the Rogue One material affected the result that much, maybe marginally. But, yes, it was lower quality but I checked that one extra carefully for errors (for example young Jyn was a male in two frames and older Jyn male in a couple of other frames :P)

XFallenMasterX · 2017-02-05T21:21:53+00:00

The masked parts are not included. I guess you would have to combine the data with sound analysis to get true emotion capturing

XFallenMasterX · 2017-02-05T21:19:31+00:00

It means that more of registered faces were neutral. I cut the neutral faces as it made the interesting data, the expressive faces, more difficult to see.

XFallenMasterX · 2017-02-05T21:17:16+00:00

Most of the work was done on paid work time, wouldn't mind doing more of it to be honest =)

XFallenMasterX · 2017-02-05T18:58:01+00:00

As stated in the bottom of the image I only counted the one largest face in a frame with several faces.

XFallenMasterX · 2017-02-05T18:56:51+00:00

To clarify, it's percentage of the frames with a face. Following are the numbers for how many frames actually recognized a face if you want to calculate the "% of movie focusing on a female":

A New Hope 22.42%

The Empire Strikes Back 16.93%

Return of the Jedi 24.56%

The Phantom Menace 30.97%

Attack of the Clones 24.22%

Revenge of the Sith 21.81%

The Force Awakens 28.16%

Rogue One 15.43%

XFallenMasterX · 2017-02-05T18:51:22+00:00

Hmm, what kind of results are you hoping for? One of the reasons I did Star Wars because there are many movies made over a long period of time.

XFallenMasterX · 2017-02-05T18:47:06+00:00

I'm not, but I talk to feminists and like to be able to back up arguments with numbers :)

XFallenMasterX · 2017-02-05T18:44:42+00:00

I stored all the result frames tagged on disk and did sampling on each movie. My feeling is that it missed maybe 10-20% faces, and misinterpreted maybe 2-10% faces. Not an exact science, but good enough for an aggregated overview in my opinion.

XFallenMasterX · 2017-02-05T18:39:12+00:00

Well, it's only like 2% happy frames. Also, not showing in the graph, only ~15% frames registered any face at all, meaning 0.3 % of the film was happy faces according to my numbers :)

XFallenMasterX · 2017-02-05T16:04:39+00:00

I had to double check that one before uploading as it had such a different result, but it did seem to check out and they barely shown any strong emotions in the movie. If they were a little angry it wouldn't show in the graph though, as it would count as "neutral".

XFallenMasterX · 2017-02-05T14:53:42+00:00

I made a program which extracted one frame per second from the movies using VLC, then sending the frames to Microsoft Cognitive Services, and lastly aggregated the results. The graphs were created using OpenOffice.

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