viewfuture/facenet-real-time-face-recognition

FaceNet-with-TripletLoss

My implementation for face recognition using FaceNet model and Triplet Loss. I like to implement different deep learning models architectures. I also like to read about applications and implementations of deep learning models. I have typed this code in my free time as a self learning exercise. So, if you run into some performane issue, i am not an expert , i won't be able to help you. I also don't have hardware to extensively test a heavy model like FaceNet. So, hyperparameters are not tuned at all. Only thing I can assure you is that this implementation works.

Medium post

Dependencies

• python 3.6
• tensorflow v1.11
• keras
• openCV

Usage

1. Create a dataset of faces for each person and arrange them in below order.

root folder 
│
└───Person 1
│   │───IMG1
│   │───IMG2
│   │   ....
└───Person 2
|   │───IMG1
|   │───IMG2
|   |   ....

2. Use align_dataset_mtcnn.py to prepare our dataset for training. Run the following command:

python align_dataset_mtcnn.py ./YOUR_DIRECTIORY_CONTAINING_DATA ./cropped

example:

3. Run train_triplet.py to train the model. Make changes (if you want) in parameters.py to adjust training parameters.

4. Run webcamFaceRecoMulti.py to recognize faces in real time. Note- Our dataset must have some images for this script to work.

Known issues/limitations:

• The dataset must contain the images of atleast two different people to train the model. You can define your own generator function to train it for single person
• Generator function might be slow when dataset has images of fewer number of people.
• Code needs some refactoring.

Note:

• It is not state of the art technique. So, dont't expect much from it.

• Model is trained using triplet loss. According to experiments it is recommended to chose positive, negative and anchor images carefully/manually for better results. Here I used a generator which selects images for positive, negative and anchor randomly (I'm Lazy af). To know more about this I recommend you to watch this video.

Refrences

• FaceNet: A Unified Embedding for Face Recognition and Clustering : https://arxiv.org/abs/1503.03832.
• Deepface paper https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf.
• deeplearning.ai 's assignments.
• https://github.com/davidsandberg/facenet