Who is more popular?

You may already know there are more pop singers in Japanese and South Korea show up in a team. There are several reasons: hitting more audiences with minimal efforts and somehow gathering the power of top performers to bring up the exposure of inexperienced or junior ones.

If you are one of a fan of one member from some group, you might be curious about the ranking of the one from entire members of the group. It’s can be easily searching the web to get the ranking from the general poll, but actually, there is a cue how the companies behind these groups see the weighting of each member.

Let’s choose one team for this. First, we need to prepare the movie file of the target team performs. It can be a live performance or MV. Downloading it is not difficult from youtube. I chose OH MY GIRL’s SSFWL for the first example. It is a live performance broadcasted from TV. JDownloader is here for help.

The next step is to prepare the model. The face recognition model in Scene Selector is trained from supervised learning. Each model identifies faces only what have trained. Every face which is not in the original dataset will be identified as the closest one unless you enable the L2 filter and it’s apart from the dataset enough. It should be fine in this use case. Preparing model is very easy but preparing data might need some effort. If you are familiar all members you about to work on, extracting faces from the video working on gives the best accuracy . The con is you have to separate some of them yourself. Please notice, Apple’s face recognition on the live stage does not work very well on live stages with complicated light conditionals.

Building up a model from a known source maybe the easiest way to collect celebrity headshots. Some would collect them from popular search engines like Google or Bing, however, from the real experiences show the search results might not be so good especially for the ones that are not very popular. The better idea is to retrieve the photo from their fans. Fans rarely mistake their idols with others. I prefer to download them from the boards of Pinterest created especially for one specific person. Scene Selector provides an import option for the organized folder and you can ignore “group pictures” that need you manually pick the correct one.

Name the folders to the identifiers you want to use and put them in the same location. So they can be imported and with section automatically created.

You are ready to use the “Create Face Classification Model” to create one. Before that, it would be better to use “Reduce Samples” to assign the collective samples together. We have found the following steps would work best to create a general model:

  1. Train with “Use Unassigned For Validation”.
  2. Save the model and use it to identifier all faces at 25% confidence.
  3. Assign mismatched faces with “Assign Prediction Mismatch”.
  4. Train with “Use Unassigned For Validation” again.

For the first step, you can let the trainer create validation data from 15% of each group if you have enough samples. You always want the deviation to be assigned for training in the last step.

In a small group, reaching 100% validation accuracy is not difficult. If you find some never fit in, you can add duplicate copy to increase the weight on the sample. Beware of overtraining. 90% is usually good enough for non-serious tasks. You should not rely on the model for serious tasks anyway.

OK, it’s the moment of the truth. Run the classification one time with the newly created model. We got this for the statistic button.

That’s it. It should be noticed it is impossible to avoid the error caused by motion blur for this type of video.

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