Welcome to the Facial Recognition System repository! Our Python-based toolkit simplifies facial recognition tasks, incorporating functionalities for training, testing, validation, and real-time recognition - both with and without object tracking. Dive into our scripts for handling both image and video data effortlessly.

1. Clone the repository: Get started by cloning this repository to your local machine.

2. Installation: Before you begin, make sure you have Python 3.7 or newer installed. Install the necessary dependencies by running:

pip install -r requirements.txt

Key Dependencies:

• OpenCV (cv2) == 4.6.0.66
• face_recognition

Get started with your data in two easy steps:

1. Use Your Own Data:

• Please place your images in the correct folders:

• training: Images for training, sorted by person or category.

• Validataion: Images for Validation, without sorting, direct images.

• test: Images for testing, again sorted by person or category.

2. Or Use the provided default dataset:

• A celebrities dataset is included for quick testing.

Please refer to the folder structure below for more details.

To train your model, please place the images in the training/ directory with right format:

python detector.py --train

The encodings will be saved in the output folder, which can be used further.

For testing on a single image:

python detector.py --test -f path/to/test_image.jpg

To validate and process multiple images:

python detector.py --validate

For live facial recognition using a webcam or video feed:

python detector.py --live

Evaluate the model on the test dataset:

python detector.py --evaluate

face_recognizer/
│
├── output/
│
├── training/
│ └── class1/
│       ├── img_1.jpg
│       └── img_2.png
│ └── class2/
│       ├── img_1.jpg
│       └── img_2.png
|
├── test/
│ └── class_name/
│       ├── img_1.jpg
│       └── img_2.png
│
├── validation/
│       ├── image1.jpg
│       └── image2.jpg
│
├── detector.py
├── extract_faces.py
├── image_process.py
├── lite_tracking.py
├── lite.py
├── tracking.py
├── video_process_tracking.py
└── video_process.py

1. Detector.py: Detector.py is a versatile script that can perform various facial recognition tasks:

   • Training and Encoding: Train the model on input data and save encodings for future use.

   • Testing: Test on a single image to recognize faces.

   • Validation: Validate on multiple images, visualize them (use 'Q' to move to next) and save the processed images with recognized faces at validation_output.

   • Live Recognition: Perform real-time face recognition using a webcam or video feed.

   • Evaluate: Evaluate the model on the test dataset to find the accuracy!

2. image_process.py: 'image_process.py' processes images from the validation folder, predicts on them, and stores the results in the output folder.

3. video_process.py: 'video_process.py' takes video input, performs facial recognition on frames, and saves the output in the output folder. It only does facial recognition on the frames.

4. video_process_tracking.py:video_process_tracking.py is an enhanced version of video_process.py with a two-phase approach: tracking and recognition.

   • Faster Tracking: This script waits for a match and seamlessly switches to the tracking phase, providing a quicker and more efficient tracking experience.

   • Improved Performance: Designed for tracking a single person, video_process_tracking.py optimizes the recognition process, making it faster and more suited to scenarios where tracking is a priority.

• Two Phases:
  • Tracking Phase: Once a match is found, the script enters the tracking phase, enhancing the accuracy and speed of tracking.
  • Recognition Phase: When tracking is lost, the script smoothly transitions back to recognition, ensuring continuous monitoring.

5. Lite.py: 'Lite.py' is a simplified version of 'Detector.py', focusing on real-time recognition. It loads pre-trained encodings and matches faces in a live video feed.

6. Tracking.py: Tracking.py is a script with two main phases:

   • Face Recognition Phase: Recognize faces and, when a match is found, initiate tracking.
   • Object Tracking Phase: Track recognized faces and store tracking data in a database file (CSV).

7. Lite_tracking.py: Lite_tracking.py is a more streamlined version of Tracking.py, emphasizing live recognition and tracking.

Feel free to explore each script for detailed instructions and usage.

Happy Recognizing!