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Face-Unlock

PDF Report View Report on W&B View Slides

This is Face-Unlock repository of IvLabs and contains the implementation of Triplet Network and FaceNet with ResNet as the backbone architecture implemented from scratch to perform one-shot and zero-shot learning on different datasets.

ivpreds

Our work is categorized as following:

  • Triplet Network

    • Triplet Loss on MNIST
    • CNN on AT&T Dataset
    • ResNet on AT&T Dataset

  • FaceNet

    • AT&T Dataset
    • LFW Dataset
    • Glint360k Dataset

  • Real-Time Face Recognition

    • Hosting Web based implementation
    • Integrating with Rasberry Pi

Datasets

  • The AT&T face dataset

    There are 10 different images of each of 40 distinct subjects.

    Dataset Statistics

    1. Color: Grey-scale
    2. Sample Size: 92x112
    3. #Samples: 400

  • Labeled Faces in the Wild

    Labeled Faces in the Wild is a public benchmark for face verification, also known as pair matching.

    Dataset Statistics

    1. 13233 images
    2. 5749 people
    3. 1680 people with two or more images

ResNet

A part of this project was also to understand and implement Residual Networks from scratch which can be found in model.py

References