These two applications store the classifier in the different file format.įor training, we need a set of samples. OpenCV provides two applications to train cascade classifier opencv_haartraining and opencv_traincascade. There are two primary states of the cascade image classifier first one is training and the other is detection. We can train the classifier for any object like cars, planes, and buildings by using the OpenCV. OpenCV provides the trainer as well as the detector. We select the features with a minimum error rate, which means these are the features that best classifies the face and non-face images.Īll possible sizes and locations of each kernel are used to calculate the plenty of features. There may be errors and misclassifications. It founds the best threshold which will categorize the faces to positive and negative. Every image is given equal weight at the starting. We apply every feature of the algorithm on all the training images.
In face detection, image features are treated as numerical information extracted from the pictures that can distinguish one image from another. Positive images are those images that consist of faces, and negative images are without faces. The HAAR cascade is a machine learning approach where a cascade function is trained from a lot of positive and negative images. Here we will learn about face detection using the HAAR cascade algorithm. There are various algorithms of face detection and face recognition. The facial image is already extracted, cropped, resized, and usually converted in the grayscale. First, it is necessary to understand the difference between face detection and face recognition.įace Detection: The face detection is generally considered as finding the faces (location and size) in an image and probably extract them to be used by the face detection algorithm.įace Recognition: The face recognition algorithm is used in finding features that are uniquely described in the image. These factors highly affect the accuracy of the computer to recognize the face more effectively. There are various complexities, such as low resolution, occlusion, illumination variations, etc. It is an effortless task for us, but it is a difficult task for a computer. A human can quickly identify the faces without much effort. The face recognition is a technique to identify or verify the face from the digital images or video frame. Next → ← prev Face recognition and Face detection using the OpenCV
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