Software Acceleration of the Deformable Shape Tracking Application: How to eliminate the Eigen Library Overhead

Shape tracking is based on landmark detection and alignment. Open-source code and pre-trained models are available for an implementation that is based on an ensemble of regression trees. The C++ Deformable Shape Tracking (DEST) implementation of face alignment that is using Eigen template library for algebraic operations is employed in this work. The overhead of the C++ Eigen library calls is measured and selected computational intensive operations are ported from Eigen implementation to custom C code achieving a remarkable acceleration in the shape tracking application. An important achievement of this work is the fact that the restructured code can be directly implemented with reconfigurable hardware for further speed improvement. Driver drowsiness and distraction detection applications are exploiting shape tracking by measuring landmark distances in order to detect eye blinking, yawning, etc. Fast video processing and accuracy is mandatory in these safety critical applications. The modified software implementation of the original DEST face alignment method presented in this paper, is almost 250 times faster due to the custom implementation of computational intensive vector/matrix operations and rotations. Eigen library is still used in non-time critical parts of the code for compact description and higher readability. Flattening of nested routines and inline implementation is also used to eliminate excessive argument copies and data type checking and conversions.