Issue with Inter-eye distance (facial recognition)

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SUMMARY

The discussion focuses on the role of Inter-eye distance (IED) in facial landmark detection, specifically in face alignment methods. Users reference the GitHub repository for supervised descent, which utilizes IED for normalization. The conversation highlights the need for accessible resources to understand IED's significance and its application in facial recognition. Additionally, participants share tools like MorphThing and OpenFace to aid beginners in exploring facial recognition concepts.

PREREQUISITES
  • Understanding of facial landmark detection techniques
  • Familiarity with normalization methods in image processing
  • Basic knowledge of facial recognition algorithms
  • Experience with open-source software tools for machine learning
NEXT STEPS
  • Research the role of Inter-eye distance in facial recognition algorithms
  • Explore the documentation of OpenFace for practical applications
  • Learn about normalization techniques in image processing
  • Investigate additional resources on facial landmark detection from MIT articles
USEFUL FOR

Beginners in facial recognition, machine learning enthusiasts, developers working on image processing applications, and researchers interested in facial landmark detection techniques.

Huyanyinglei
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TL;DR
face alignment and inter-eye distance
I have the following problem. I’m a beginner of facial landmark detection or so-called face alignment. I see others use Inter-eye distance when they’re doing face alignment. E.g., this: https://github.com/patrikhuber/superviseddescent/issues/56, uses IED for normalization. I wonder what role the IED plays in face alignment methods in general, why and when we need to do it and how we can do it.
May I ask if there’s some good, easy-to-read articles that can help beginners like me understand this question? After some google search I didn’t find much about it. Thanx in advance.
 
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You can upload a face image to morphthing.com, and click on points of reference, and then check out the result as the image is combined with another that's in their library, or with another that you've uploaded and selected points for -- it's fun even to just play around with A+B combinations from their library, but doing the point selection is more instructive in giving you some idea of what must be happening in the processes -- e.g. watch how wonky it looks if you deliberately pick off-target points.
 
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You can find some accessible MIT articles here: https://www.technologyreview.com/artificial-intelligence/face-recognition/

Facial recognition is not easy -- one encounters complex versions of Sayre's Paradox -- but take heart -- you phone has much more processing and storage capacity than had the IBM machines that were available to Prof. Sayre in 1973.

Here's a link for some open source facial recognition software: https://cmusatyalab.github.io/openface/

If you read some of the documentation there, you'll gain a deeper understanding.
 

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