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A quick recap

In the first part of this article, we saw how we can initialize our default camera, get image frames from it and carry out face detection.
To complete the scope of this article, we would be taking a step further. We would look at how to store training data in a structured database, train your face recognition engine and use to engine to predict faces detected in images.

Store Training Data (Using SQLite)

The storage of training data would be demonstrated using SQLite due to the fact that it is easy to use. SQLite is a software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine. It removes the hassle of client-server communication when it comes to using databases.
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Introduction

First off, Face detection and Face recognition are two totally different things although one builds upon the other (recognition builds upon detection). Detection is the process by which the system identifies human faces in digital images, regardless of the source while Recognition is the identifying a known face with a known name in digital images, still regardless of the source. The source can range from a scanned copy of a photograph to a live video stream. Face detection and recognition is a section of Machine learning with a good number of research topics focused on improving the existing algorithms.
This article intends to show the reader how to use EmguCV 3.0 for Face detection and recognition in C#, emphasis on 3.0 because a lot of changes have been made to the library since 2.x versions, and a lot of tutorials/articles (as at the time of writing) focus on the 2.x versions of the library.
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