Face Recognition - Realtime Recognition

Table of Contents

1. Introduction

2. Requirement

3. Usage

4. Reference

1. Introduction

• Face recognition is a technology capable of matching a human face from a digital image or a video frame against a database of faces, typically employed to authenticate users through ID verification services, works by pinpointing and measuring facial features from a given image.
• Recognize and manipulate faces with Python and its support libraries. The project uses MTCNN for detecting faces, then applies a simple alignment for each detected face and feeds those aligned faces into embeddings model (Facenet). Finally, a softmax classifier and cosine similarity method was put on top of embedded vectors for classification task.

2. Requirement

• Tensorflow-gpu (2.1.0)
• Python 3.7+
• Keras 2.2.4
• OpenCV-python (4.4.0)
• Pip or anaconda

Install MTCNN:

pip install mtcnn (for pip)
conda install -c conda-forge mtcnn (for conda)

3. Usage

Crawl data:

python src/save_data.py --name (name of save_dir)

Make sure your computer has a webcam

Preprocessing:

• Train embedding model with your dataset, you need to organize dataset as follows:

Each person needs 4-5 raw photos with many different angle shooting: front, left, right, ...

Face-Recognition
└───data/
      └───person1/
      |      └───person1_1.jpg
      |          person1_2.jpg
      |          .....
      └───person2/
      |      └───person2_1.jpg
      |          person2_2.jpg
      |          .....
      └───personN/
             └───personN_1.jpg
                 personN_2.jpg
                 .....

Augment data:

• After having the raw image files, run augment_data.py to augment more images, each person after running will have 100 different images. Augments: rotation_range = 15, brightness_range=[0.4,1.5], horizontal_flip

python augment_data.py

Train embedding model:

python train_embs.py

After embedding, embedded file will be saved to output / train_embs.pickle

Train softmax model:

python train_classify.py

The number of classes i'm setting here is 37 classes, so change the number of classes that match your dataset

Enjoy result:

Label is name of saved-dir - box is green, unknown will don't have label - box is red. Here label is id of person.

• For image recognition:

python image_recognition.py --path (path to image) --facedetect (use MTCNN to detect face before recognition - yes/no)

Output will be saved to output/(name_of_img.jpg)

• For video recognition:

python video_recognition.py --path (path to video) --facemodel (path of facenet weights) --classifymodel (path of classify model weight) --embspath (path of embed dir)

• For stream recognition:

python stream_recognition.py

Make sure your computer has a webcam, here i'm setting webcam 0

4. Reference

• Insightface
• Facenet
• MTCNN
• Facenet-keras model (trained by MS-Celeb-1M dataset): Download