Haar cascade

1. What is a Haar cascade?
2. How does Haar cascade work for face detection?
3. Is haar cascade machine learning?
4. What is Haar in machine learning?

What is a Haar cascade?

Haar cascade is an algorithm that can detect objects in images, irrespective of their scale in image and location. This algorithm is not so complex and can run in real-time. We can train a haar-cascade detector to detect various objects like cars, bikes, buildings, fruits, etc.

How does Haar cascade work for face detection?

What is Haar Cascade, and how it works? Haar Cascade is a feature-based object detection algorithm to detect objects from images. A cascade function is trained on lots of positive and negative images for detection. The algorithm does not require extensive computation and can run in real-time.

Is haar cascade machine learning?

Haar Cascading is the machine learning method where a classifier is drilled from a great deal of positive and negative photos. The algorithm is put forwarded by Paul Viola and Michael Jones [5, 6]. Haar feature-based cascade classifiers are the classifiers implemented for object detection.

What is Haar in machine learning?

Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier. Positive images – These images contain the images which we want our classifier to identify. Negative Images – Images of everything else, which do not contain the object we want to detect.