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Implementation for paper, "Learned Features are better for Ethnicity classification"

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InzamamAnwar/Learned-Features-are-better-for-Ethnicity-Classification

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Learned Features are better for Ethnicity Classification

This repository provides implementation for the paper, Learned Features are better for Ethnicity Classification. It is recommended to go through the Deep Face Recognition paper also.

Prerequisites

Following are the softwares and tools needed to work with this repository.

  1. Python 3.5 or 3.6
  2. Matlab 2015 or above

Code presented here is tested with Python 3.6 and Matlab 2016

Installation

After installing Python and Matlab following are the packages that should be installed

  1. Python Packages (OpenCV, dlib, numpy)
  2. Matlab Toolbox(MatConvNet).     

To install the MatConvNet follow the instructions given here

Running the code

  • Download or clone the repository
  • Create a new folder named, VGG Network in the main porject direcotry
  • Download the pretrained VGG Face Network from here in VGG Network folder
  • Copy images into the Input_Images folder. Some example images are already included
  • Open the main.m in Matlab and run the code
  • Output class of image will be saved in result with image name and class name

License

Open source code snippets used in this project are Licensed. For details see Licenses folder

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Implementation for paper, "Learned Features are better for Ethnicity classification"

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