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What is the limit for number of classes in a multi-class classification using OpenCV

asked 2020-03-07 08:06:57 -0600

vidyaa gravatar image

I am currently working on a project where we have to build a model which can detect and recognize faces in a live-stream. I am using Python, OpenCV, and Haar Cascade Classifiers(HCC) for this project.

i have stumbled upon following questions while doing this:

  1. How many different number of people(or classes) can OpenCV/HCC accurately detect if memory isn't a problem?

  2. If I want to detect up to 10,000(or even a bigger number) different people, what are the difficulties I will come across using this technology which is fairly simple compared to other Deep Learning Models like ResNet, etc.?

  3. If I cannot store images on my local machine when I scale up the number of ppl to be detected, what might be the best solution?

Add additional inputs if you have any. Would be of great help. Thank you.

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answered 2020-03-07 08:49:53 -0600

berak gravatar image

updated 2020-03-07 08:54:01 -0600

  1. cascades are binary classifiers (e.g: face or not) you cannot use them to recognize different ppl.
  2. again, it cannot work, you will need to use something else
  3. use a pretrained cnn like facenet or spherenet, that transforms face images into small (128/512) feature vectors, which can easily be stored into a (network ?)db, and compared with simple L2 or cosine distance
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Asked: 2020-03-07 08:06:57 -0600

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Last updated: Mar 07 '20