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NovatecConsulting

FaceRecognition-in-ARKit

Detects faces using the Vision-API and runs the extracted face through a CoreML-model to identiy the specific persons.

ApplicationsObject detectionSwift
Open on GitHub
Momentum
+0stars in 24 hours0.0%
835
Stars
108
Forks
+0
This week
1
Contributors
Created 2017-09-03 · Updated 2026-09-05 · #13337 today
Top developers
m-ruhl

NovatecConsulting/FaceRecognition-in-ARKit is ranked 13,337 with 835 stars.

Same-category comparison

NovatecConsulting/FaceRecognition-in-ARKit is ranked 13,337 among these 8 peers; saharmor/whisper-playground is ranked 13,335.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageSwift
CategoryApplications
SubcategoryObject detection
KeywordsLocalAI
Stars835
Forks108
Contributors1
Created date2017-09-03
Updated date2026-09-05
Stars in 1 day0
Stars in 7 days0
Rank13,337
Previous rank13,239
Rank change-98
7-day stars rank13,894
Previous 7-day stars rank13,913
7-day stars rank change19
Total stars rank12,873
Previous total stars rank12,850
Total stars rank change-23
CountryGermany
Frequently asked questions

How many stars and forks are recorded?

NovatecConsulting/FaceRecognition-in-ARKit has 835 stars and 108 forks.

What is the repository rank?

NovatecConsulting/FaceRecognition-in-ARKit is ranked 13,337.

What language and category are recorded?

NovatecConsulting/FaceRecognition-in-ARKit has 835 stars, uses Swift and belongs to Applications.

Which repository is closest in rank?

NovatecConsulting/FaceRecognition-in-ARKit is compared with saharmor/whisper-playground, ranked 13,335 with 836 stars.

README

FaceRecognition in ARKit

This is a simple showcase project, that detects faces using the Vision-API and runs the extracted face through a CoreML-model to identiy the specific persons.

Requirements

  • Xcode 9
  • iPhone 6s or newer
  • Machine-Learning model

Machine-Learning model

To create your own machine-learning model, you can read our blog post "How we created our Face-Recognition model"

The short version is:

  • We trained a model in the AWS using Nvidia DIGITS
  • Took a couple of hundred pictures of each person, and extracted the faces
  • Also added an "unknown" category with differnent faces.
  • Used a pretrained model fine-tuned for face-recognition.

Acknowledgements

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