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Face-heaan

1. Overview

  • To implement a face ID using homomorphic encryption.

    Screenshot 2023-05-20 at 2 20 35 PM

    Overall Structure

    • Face Identification
    • Face Registration
    • Similarity Measurement : Cosine Similarity, Manhattan Similarity, Euclidean Similarity
    • Webcam Inference
  • Demo Video

2. Environment

  • You must need a camera connected on your environment.
  • Python version is 3.9.
  • Please notice that we assume that you are using 'PyTorch' and device type as 'cpu'.
  • Installing all the requirements may take some time. After installation, you can run the codes.
  • Specific methods for setting preferences are described in #4.

3. Pre-Trained Model Download

  • You need to download pretrained model for implementation.

  • Download Link / Password : b2ec

  • The pretrained model use resnet-18 without se.

  • Please modify the path for the pretrained model.

  • A correct location for the pre-trained model is shown below.

    ./checkpoints/resnet18_110.pth
    

4. Installation

  • Please download pytorch 1.12.0 depending on your OS. Instruction is in here.

  • requirements.txt file is required to set up the virtual environment for running the program. This file contains a list of all the libraries needed to run your program and their versions.

    1. In venv Environment,

    $ python -m venv [your virtual environment name]
    
    $ source [your virtual environment name]/Scripts/activate
    
    $ pip install torch==1.12.0 #for Mac
    
    $ pip install -r requirements.txt
    
    • Create your own virtual environment.
    • Write the commands to activate the virtual environment and install the necessary libraries.
    • You have a 'requirements.txt' file that lists the required libraries, use the command pip install -r requirements.txt to install libraries.

    2. In Anaconda Environment,

    $ conda create -n [your virtual environment name] python=3.9
    
    $ conda activate [your virtual environment name]
    
    $ conda install pytorch==1.12.0 -c pytorch #for Mac
    
    $ pip install -r requirements.txt
    
    • Create your own virtual environment.
    • Activate your Anaconda virtual environment where you want to install the package. If your virtual environment is named 'piheaanenv', you can type conda activate piheaanenv.
    • Use the command pip install -r requirements.txt to install libraries.
  • If you encounter any conflicts, please check your dependencies carefully and reinstall according to your running environment.

5. Usage

You need to run inference_heaan.py file.

  1. Face registration

    Screenshot 2023-05-20 at 3 17 07 PM
  • You can press space bar to proceed face registration.
  1. Result

    Screenshot 2023-05-21 at 12 20 45 PM
    • Unlock : When registered face is detected.
    • Lock : When registered face is not detected.
    • Too many faces : When many faces are detected.

6. Change Measurement Method

# 1) cosine similarity measurement
res_ctxt = he.cosin_sim(ctxt1, ctxt2)
result = he.compare('cosine', cos_thres, res_ctxt)

# # 2) euclidean distance measurement
# res_ctxt = he.euclidean_distance(ctxt1, ctxt2)
# result = he.compare('euclidean', euc_thres, res_ctxt)

# # 3) manhattan distance measurement
# res_ctxt = he.manhattan_distance(ctxt1, ctxt2)
# result = he.compare('manhattan', man_thres, res_ctxt)
  • In inference_heaan.py file, you'll find codes like the one above. When you download and run the code, it is currently set to the cosine similarity measurement technique.
  • If you want to change to the euclidean similarity measure, you can comment out the rest of the block and uncomment the second and third lines of the second block.
  • If you want to change to manhattan similarity measurement, you can comment out the rest of the blocks and uncomment the second and third lines of the third block.

7. Reference

  • Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4690-4699).

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FaceID using pi-heaan (CKH)

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