An Open-Source High-Throughput, Reduced Memory Footprint, Face Detection, Pose Estimation and Landmark Localization System

Abstract

Face Detection, Pose Estimation and Landmark Localization are all considered important vision processes and are very widely utilized in several applications ranging from security/safety to automotive and assisted living. In this paper we present an open-source optimized implementation of a system addressing all those processes. In particular, we optimized both the memory requirements and the performance of the very widely utilized Tree Structure Model (TSM), which is the main core in all those tasks. Several optimizations have been proposed so as to increase the performance in both uni-and multi-processor systems, while also reducing the memory footprint so as to allow for the implementation of those schemes, for the first time, in embedded systems. The proposed system is at least 100% faster (and up to 300%) and requires more than 10 time less memory than the existing implementations. Since the algorithm implemented is one of the most widely used in the area of face detection and we distribute the optimized code in an open-source manner, we believe that it can act as an important reference implementation for any similar system proposed.