A Lightweight and Accurate Face Detection Algorithm Based on Retinaface

Face recognition is widely used in people's daily life. The face recognition mentioned in this paper is not for the recognition of individual faces, but refers to localization of faces in pictures or videos and counting of faces. The development of face detection algorithms can be divided into three phases, namely the early algorithms, the Adaptive Boosting framework [1], and the deep learning era. Early face recognition used a modular matching technique, which involves using a template image of a face to match various locations in the detection image to determine if there is a face at that location. A representative work was the algorithm proposed by Rowley (Neural network-based face detection[2]), which used a 20x20 dataset to train a Multi-layer Perceptron [3] model with good accuracy, but ran slowly. In 1997, Margineantu et al. proposed a face recognition algorithm in the AdaBoost framework. The boost algorithm is an ensemble learning algorithm based on PAC (probably approximately correct) learning theory. In 2001, Viola and Jones designed a face detection algorithm [4] It used simple Haar-like [5] features and cascaded AdaBoost classifiers to construct a detector that improved detection speed by two orders of magnitude over previous methods and maintained good accuracy. This approach is known as the VJ framework.