This ipython notebook is a implementation of face detection using MTCNN and OpenCV Haar Cascade.
Face detection is the process of identifying or verifying the presence of human faces in digital images or videos. It is a key technology behind advanced driver assistance systems (ADAS) and is used in many applications such as security, human-computer interaction, image and video indexing, and mobile photography.
There are several methods for face detection, including Haar cascades, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLO). Each of these methods uses a different algorithm and has its own advantages and disadvantages. Haar cascades, for example, are fast and efficient, but may not be as accurate as some other methods. Other methods like deep learning based methods (MTCNN, RetinaFace etc) are more accurate but computationally expensive.
It is important to note that face detection is different from face recognition which is the process of identifying a person from a digital image or video frame.
MTCNN stands for Multi-task Cascaded Convolutional Networks. It is a face detection algorithm that uses a cascaded architecture to detect faces and facial landmarks such as the eyes, nose, and mouth. It is trained to detect faces in images and is able to handle a wide range of face poses and scales. It is a popular algorithm for face detection and has been used in many applications such as face recognition and facial expression analysis.
Haar cascade is a machine learning object detection method used to identify objects in images or video. It is trained from positive and negative images. Haar cascades are trained from positive images containing the object of interest and negative images not containing the object. The algorithm is based on the Haar wavelet, which is used to analyze the image and detect features that correspond to the object. The trained classifier is then used to detect the object in new images. Haar cascades are commonly used in face and eye detection, and have been implemented in OpenCV library.