Face-Landmark-Tracker

FaceLandmarkTracker is a high-performance face landmark detection and tracking library built on OpenCV and DeepCore (Deepixels proprietary library). It leverages TensorFlow Lite models for real-time face detection, landmark extraction, and head pose estimation. A Python wrapper is included for easy integration.

FaceLandmarkTracker outputs an array of 106 keypoints corresponding to facial landmarks. Each landmark has a fixed index, which you can use to identify facial regions such as eyes, nose, lips, and jawline.

Below is an illustration showing the landmark indexing scheme:

(Example: numbers correspond to keypoint indices returned by get_keypoints())

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Features

• Real-time face landmark tracking
• Face bounding box extraction
• Head pose estimation
• Visibility confidence per keypoint
• Debug visualization of landmarks
• Easy Python integration
• Works on CPU only — no GPU required
• Supported Python versions: 3.9, 3.10, 3.11, 3.12

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Installation

Install via pip using the .whl file that matches your Python version:

# Python 3.10
pip install deeppy-2.19.459-cp310-cp310-win_amd64.whl

# Python 3.11
pip install deeppy-2.19.459-cp311-cp311-win_amd64.whl

Make sure the cpXXX in the filename matches your Python version.

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Performance

FaceLandmarkTracker is optimized for real-time performance on CPU. Typical inference speeds:

Environment	Resolution	FPS
Noteboook CPU (Intel i7 11th Gen)	640x480	200
Desktop CPU (Intel i7 11th Gen)	640x480	330

• No GPU required — runs efficiently on modern CPUs
• Real-time performance with webcam streams
• Benchmarks may vary depending on CPU model and input resolution

⚠️ Note: Performance may be slightly lower when displaying debug visualization.

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Python Usage

1. Example Python code for capturing and processing a live camera stream.

import cv2
from deeppy import FaceLandmarkTracker

# Path to your license file
license_path="dp_face_2025.lic"

def run_face_tracker_camera():
    dp_face = FaceLandmarkTracker()
    dp_face.init(license_path)

    cap = cv2.VideoCapture(0)
    if not cap.isOpened():
        print("Failed to open webcam.")
        return

    while True:
        ret, frame = cap.read()
        if not ret:
            break

        dp_face.run(frame, 0.2, False)
        print("Keypoints:", dp_face.get_keypoints())
        print("Rect:", dp_face.get_rect())
        print("Pose:", dp_face.get_pose())

        frame = dp_face.display_debug(frame)
        cv2.imshow("FaceLandmarkTracker", frame)

        if cv2.waitKey(1) & 0xFF == 27:  # ESC key
            break

    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    run_face_tracker_camera()

2. Example Python code for processing images.

import cv2
from deeppy import FaceLandmarkTracker

# Path to your license file
license_path="dp_face_2025.lic"

def run_face_tracker_image(image_paths):
    dp_face = FaceLandmarkTracker()
    dp_face.init(license_path)

    for path in image_paths:
        frame = cv2.imread(path)
        if frame is None:
            print("Failed to load image.")
            return
    
        dp_face.run(frame, 0.2, True)
        print("Keypoints:", dp_face.get_keypoints())
        print("Rect:", dp_face.get_rect())
        print("Pose:", dp_face.get_pose())

        frame = dp_face.display_debug(frame)
        cv2.imshow("FaceLandmarkTracker", frame)

        if cv2.waitKey(0) & 0xFF == 27:  # ESC key
            break

    cv2.destroyAllWindows()

if __name__ == "__main__":
    image_paths = ["example.jpg","example2.jpg"]
    run_face_tracker_image(image_paths)

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API Reference

Initialization

license_path = "dp_face_2025.lic"

dp_face = FaceLandmarkTracker()
if dp_face.init(license_path):
    print("Model loaded successfully")
else:
    print("Model initialization failed")

• license_path – Path to the required license file (.lic).

• init(license_path) – Loads the face tracking models using the given license.

  • Returns True if the models are loaded successfully.
  • Raises an exception if the license file path is invalid or the license is not valid.

Tracking

dp_face.run(image_src, fThresh, isStill)

• image_src – input image (numpy array)
• fThresh – confidence threshold, the common value is around 0.2
• isStill – whether image is static

Get Results

keypoints = dp_face.get_keypoints()
rect = dp_face.get_rect()
visibility = dp_face.get_visibility()
pose = dp_face.get_pose()

• get_keypoints() – returns 106 x 2 array of facial landmarks
• get_rect() – returns bounding box [x, y, width, height] where x,y is the top left corner of the bounding box.
• get_visibility() – visibility confidence per landmark
• get_pose() – estimated head pose [pitch, yaw, roll]

Visualization

image_out = dp_face.display_debug(image_src)

• Draws landmarks, bounding boxes, and pose axes on the image

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License

This library is proprietary and requires a paid license. You may not use, distribute, or modify it without a valid license.

How to Get a License

• Contact us via email: support@deepixel.xyz

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Image Credit

The face image used in this README/demo was generated by This Person Does Not Exist. This image is synthetic and does not depict a real person.

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