Several deep learning-based face detection algorithms have been developed in recent years. This is the explanation of a few of the well-known models.

Haar Cascades, one of the earliest face recognition techniques, locates faces in pictures using a cascade classifier and Haar features. Although it is not a deep learning model, it has been widely used and can nevertheless provide decent accuracy.

 Convolutional Neural Networks (CNNs): For tasks involving the detection and recognition of objects, including faces, CNNs have become the de facto approach. A few well-known CNN-based face detectors are the Single Shot Multibox Detector (SSD), Region-based Convolutional Neural Network (R-CNN), and Faster R-CNN.

 The You Only Look Once (YOLO) model for real-time item detection uses only one neural network. It has been modified to accurately detect faces, and preliminary testing seem promising.

The well-known face detector MTCNN (Multi-task Cascaded Convolutional Networks) recognises faces at different scales using a cascade of CNNs. It is well known for its great accuracy and efficiency and has been frequently used in facial recognition systems.

RetinaFace, a cutting-edge face detector, uses a multi-task learning architecture to simultaneously recognise faces, facial landmarks, and gender. It is often used in real-world contexts and has shown exceptional performance on benchmark datasets.