Object Tracking has primarily been characterized as the study of object motion trajectory over constraint subspaces under attempts to mimic human efficiency. However, the trend of monotonically increasing applicability and integrated relevance over distributed commercial frontiers necessitates that scalability be addressed. The present work proposes a system for fast large scale facial tracking over distributed systems beyond individual human capabilities leveraging the computational prowess of large scale processing engines such as Apache Spark. The system is pivoted on an interval based approach for receiving the input feed streams, which is followed by a deep encoder-decoder network for generation of robust environment invariant feature encoding. The system performance is analyzed while functionally varying various pipeline components, to highlight the robustness of the vector representations and near real-time processing performance. © 2017, Springer International Publishing AG.