What is ‘FaceNet’ and how does facial recognition system work?
Facial recognition is everywhere. From your cellphones to your laptops, it has been the new face in the world of security system(No pun intended! 🤭). Not only has it been the most easiest way to secure and access your system, it is also the only system where your face (which you take everywhere) as a security system.
Deep learning is the engine behind the success of facial recognition system. Among those deep learning algorithms, facenet is one of them. Facenet is the name of facial recognition system that was proposed by Google Researchers in 2015 in the paper titled Facenet: A Unified Embedding for Face Recognition and Clustering. It has achieved state of the art results in many benchmark face recognition dataset such as Labeled Faces in the Wild(LFW) and youtube face dataset.
But before we dive deep into FaceNet and how it works, let’s take a step back and differentiate between Face verification and face recognition.
What is Face Verification?
- Input image, name/ID
- Output whether the input image is that of a person that is claimed to be.
What is Face Recognition?
- Has a database of n different person
- Gets an input image
