#025 FaceNet: A Unified Embedding for Face Recognition and Clustering in PyTorch - Master Data Science 05.01.2022

Highlights: Face recognition represents an active area of research for more than 3 decades. This paper, FaceNet, published in 2015, introduced a lot of novelties and significantly improved the performance of face recognition, verification, and clustering tasks. Here, we explore this interesting framework that become popular for introducing 1) 128-dimensional face embedding vector and 2) triplet loss function. In addition to the theoretical background, we give an outline of how this network can be implemented in PyTorch. FaceNet method developed a novel design for the final layer of the CNN to embed the face image. This, so called, embedding vector is of size 128 elements.