https://github.com/rodrigocmoraes/face_recognition
Face Recognition with MTCNN
https://github.com/rodrigocmoraes/face_recognition
detect-faces face-recognition faces mtcnn tensorflow
Last synced: 6 months ago
JSON representation
Face Recognition with MTCNN
- Host: GitHub
- URL: https://github.com/rodrigocmoraes/face_recognition
- Owner: RodrigoCMoraes
- Created: 2019-01-28T00:16:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-27T19:01:06.000Z (almost 6 years ago)
- Last Synced: 2025-02-14T15:07:23.634Z (8 months ago)
- Topics: detect-faces, face-recognition, faces, mtcnn, tensorflow
- Language: Python
- Homepage:
- Size: 174 KB
- Stars: 10
- Watchers: 1
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Status of project: ON GOING
# Face Recognition
This project is based on [MTCNN](https://github.com/ipazc/mtcnn) and pretend to work with Face Recognition, where faces might has oclusion of its parts.
# Features implemented:
* Detect faces
* Draw ROI in faces
* Save faces detected
* Calculate time of operations
# Features to be implemented:
* Augment face images ([TensorFlow](https://github.com/tensorflow/tensorflow))
* Extract features(embeddings) from augmented images([FaceNet](https://github.com/davidsandberg/facenet))
* Train classifier to recognize people([KNN CUDA](https://github.com/chrischoy/knn_cuda))
# Setup used for generate this code:
* For Windows and Linux are used latest Python from [Anaconda](https://www.anaconda.com/download/#linux)
* SO versions: Windows 10 x64 Pro and Linux Mint and Ubuntu 19 x64
* Conda environment with commands:
```
# create environment
conda create -f environment.yml
# activate environment
conda activate face_recognition
```
# Demo
1. Clone this repository
2. Create environment
3. Install dependencies on environment
4. Execute script mtcnn_demo.py
```
python mtcnn_demo.py
```
## Expected Terminal output
```
(load_detector) time: 0.89s
(load_image) time: 1.29s
(detect_faces) time: 0.40s
3468
(write_image) time: 0.00s
4800
(write_image) time: 0.00s
4107
(write_image) time: 0.00s
3675
(write_image) time: 0.00s
3888
(write_image) time: 0.00s
3468
(write_image) time: 0.00s
4332
(write_image) time: 0.00s
3072
(write_image) time: 0.00s
4107
(write_image) time: 0.00s
3888
(write_image) time: 0.00s
2700
(write_image) time: 0.00s
(crop_faces) time: 0.00s
(draw_faces) time: 0.00s
(write_image) time: 0.00s
```
## Expected output images:
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