Dlib CNN Face Detection
The Convolutional Neural Network (CNN) Face Detector in Dlib is a deep learning-based approach for detecting faces in images. Unlike traditional computer vision techniques that rely on hand-crafted features, CNNs learn to extract relevant features from the input data, which makes them more effective in dealing with complex tasks like face detection. It achieved an accuracy of 97.6% on the LFW dataset. Additionally, the model is robust to variations in lighting, pose, and expression, making it suitable for real-world applications.
Example
from dronevis.models import CNNFaceDetection
model = CNNFaceDetection()
model.load_model()
model.detect_webcam()
Dlib CNN Face Detection
- class dronevis.models.CNNFaceDetection
CNN Face Detection model
- __init__()
Initialize self. See help(type(self)) for accurate signature.
- load_model()
Load model weights
- transform_img(image)
Run image transformation
- Parameters
image (np.ndarray) – Input image
- Returns
Transformed image
- Return type
np.ndarray
- predict(image)
Run model inference on the image
- Parameters
image (np.ndarray) – Input image
- Returns
Image withe face annotations
- Return type
np.ndarray