Current cutting-edge facial recognition models offer human-level accuracy. Still, we can improve facial recognition model accuracies if we represent classifications in a graph. In this post, we are going to find false negative classifications of facial recognition models with Neo4j graph database. We have just focused on detecting false positives in facial recognition with Neo4j. False positives are mis-classifications verifying different persons as same person.

Use coupon code ALMOSTFREE and get FLAT 95% discount Learn how to organize your data with the popular Neo4j graph database in this Neo4J database tutorial!! Search engines and social media platforms have propelled graph databases into the lime light. While traditional relational databases are still popular among many companies, graph databases are slowly climbing the ranks as a go to database for many complex structures. Databases play an important role when it comes to storing and fetching large amounts of data. Data is often a huge mess on the internet, which needs to be meticulously sorted into sections and sub-sections to make it easier for analyzing. Data is in raw form is useless for individuals and companies alike, until it is sorted and provides the user with information or it can specifically answer the user's question.