The Face Tracker project leverages the power of deep learning, utilizing Single Shot Detector which was fine-tuned for a custom dataset,
To take the images for the dataset run image_collection.py
To annotate the images use the labelme package
To split the images into train test and validation split run split_data.py
To train the model run each cell in order in face_detection.ipynb
Ps you only have to run the augment data cell once and comment it afterwards
Delete the .json file in the data and aug_data before doing any of the above steps
- The project employs the SSD architecture which is used for face detection in tensorflow.
- OpenCV is used to make real-time face detection
- This project so that anyone can make their own custom dataset using their webcam and their own face as data.
- Took 90 photos of myself in various scenarios and some without my face to add some noise to the dataset.
- The labelme library was used to annotate the labels for the images.
- The albumentations library was used to augment the images to around 4000 images.
The deep learning model aimed for real-time face tracking using OpenCV
tensorflow==2.8.0
albumentation==1.1.0
labelme==5.3.1
opencv-python==4.5.4.60
python==3.9