Making use of smart algorithms in managing photo collections. For instance: face and body recognition to automatically detect specific persons in images, smart hashing techniques to search and optionally remove low resolution duplicates.
We kick off with a series about Automatic Recognition & Finding People. The introduction to this theme can be found in this post: it demonstrates that the basics of facial recognition and photo intelligence are relatively easy to apply yourself! In the next post things gets serious: we not only add full-body person recognition but we also pair it with an enhanced hardened face encoder! We now have the tools to build and manage a knowledge base (KB) of known named and identifiable people in our photo collections. In later posts we added useful features to get the most out of working with such a knowledge base. It can be used for instance for batch recognition of a whole bunch of photos in one go!
First however we developed an actual application with a graphical user interface (GUI) to hold things together and make them easily manageable. The application gets menu operations to add, rename, reencode or remove people, editing the KB and show a general overview of it’s content. The complete source code (in Python) for the Person Recognition App is available for downloading.
The application has recently been enhanced with functions for interactive searching! These make use of smart techniques for optimizing performance. This proved useful adding eye candy: finding and showing lookalikes side-by-side.
The next level is reached adding meaning beyond recognition. Enriching a biometric recognition system with a structured, JSON-schema-driven People Database adds context, meaning, and long-term usefulness. In coming posts we’ll demonstrate other advanced features for photo management that can be added to our application but also used stand alone.