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.pyTrain embedding model:
python train_embs.pyAfter embedding, embedded file will be saved to output / train_embs.pickle
Train softmax model:
python train_classify.pyThe 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.pyMake 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