FaceNet
FaceNet is a face recognition system developed by researchers at Google and introduced in 2015. It presents a unified approach to face recognition and verification by learning a compact embedding for facial images. The core idea is to train a deep convolutional neural network to map each facial image to a 128-dimensional vector in Euclidean space, where the distance between embeddings corresponds to facial similarity.
The network is trained with a triplet loss: for a triplet consisting of an anchor, a positive
FaceNet uses a deep CNN architecture, often based on Inception-style backbones, to extract robust features from
FaceNet achieved strong performance on standard benchmarks of its time, such as Labeled Faces in the Wild