Face Search in a Big Data System

Big data applications are growing rapidly as more sensors are connected to the internet and gathering business critical information for processing. Imaging sensors are an important type of sensors for collecting images and video data, and are widely deployed on smartphones, video surveillance networks, and robots. Traditional databases are designed to ingest and search structured information. The analysis of unstructured information such as images and videos is often done separately. In this paper, we describe a big data system with deep integration of face analysis and recognition in images and videos. We show how we can utilize the built-in parallelization in the Vertica database system to accelerate feature computation and search. We also show an example application of the system for face re-identification and face search in images and videos.