dlib is a C++ library that provides many machine learning algorithms and tools, including face recognition. dlib's face recognition algorithm is based on deep learning technology, using Convolutional Neural Network (CNN) for training.
The face recognition algorithm of dlib is mainly divided into two steps: face detection and face feature extraction.
1. Face detection
dlib's face detection algorithm uses a method based on HOG features and cascaded classifiers. The HOG feature is a local feature descriptor that can effectively describe the edge and texture information in an image. A cascade classifier is a multi-layer classifier, each layer is a weak classifier, and a strong classifier can be obtained by cascading. dlib's face detection algorithm uses a 5-layer cascade classifier to detect faces in images of different scales.
2. Face feature extraction
dlib's face feature extraction algorithm uses a method based on deep learning. Specifically, it uses a 29-layer convolutional neural network that can convert an image of a face into a 128-dimensional vector called a face feature vector. This vector is very comparable and can be used to compare the similarity of two face images.
dlib's face recognition algorithm can judge whether they belong to the same person by comparing the Euclidean distance of two face feature vectors. If the Euclidean distance between two face feature vectors is less than a threshold, they are considered to belong to the same person. This threshold can be adjusted according to actual application scenarios.
In general, dlib's face recognition algorithm is a deep learning-based method with high accuracy and robustness. It can be applied to face recognition, face verification, face search and many other fields.