tranducminh1902/facial-recognition

Using YOLOv3 for detection and custom 3 classes trained mobilenetv2 model for prediction

Facial Recognition

Using YOLOv3 for detection and custom 3 classes trained mobilenetv2 model from tensorflow for prediction.

OpenCV and streamlit was used to deploy using webcam.

The dataset for training:

The dataset includes images of team members, captured by webcam using data_creator.py.

Each image using YOLO to crop to take only the face area.

Dataset has been put in structural folders with each folder name as label (name of team member):

• long: 597 images
• minh: 669 images
• tung: 507 images

Link for the dataset: images_260921.zip

Training the model:

MobilenetV2 model was used as based model and customized classification dense layers to predict the team members.

The model was trained using Train_model.ipynb

The best model was exported to .h5 file for deployment: model_1.h5

Deployment:

Streamlit was used to deploy the file: face_recog_app.py

• Download YOLOv3 model and weight at: https://drive.google.com/drive/folders/1QO9ydq_cUHlfpKK78DSUymHii5aix2jf?usp=sharing
• Load YOLOv3 for face detection using weights and config files
• Load trained CNN model for face prediction
• Create bounding box around each face detected and show name with confidence level (in probability)

Contributions and References:

Contributions:

• Minh Tran: https://github.com/tranducminh1902
• Long Nguyen: https://github.com/longnguyentruong0607
• Vu Thanh Tung: https://github.com/tung151078

References:

• Creating dataset: https://github.com/murtazahassan/OpenCV-Python-Tutorials-and-Projects/blob/master/Intermediate/Custom%20Object%20Detection/createData.py