A Parallel Approach for Real-Time Face Recognition from a Large Database
This addresses the problem of efficient real-time identification for security or surveillance applications, but it appears incremental as it builds on existing embedding and parallelization techniques.
The paper tackles real-time face recognition from large databases by developing a system that uses facial embeddings and parallelized searching, achieving high accuracy and scalability for thousands of embeddings.
We present a new facial recognition system, capable of identifying a person, provided their likeness has been previously stored in the system, in real time. The system is based on storing and comparing facial embeddings of the subject, and identifying them later within a live video feed. This system is highly accurate, and is able to tag people with their ID in real time. It is able to do so, even when using a database containing thousands of facial embeddings, by using a parallelized searching technique. This makes the system quite fast and allows it to be highly scalable.