Real-time face alignment: evaluation methods, training strategies and implementation optimization

Abstract

Face alignment is a crucial component in most face analysis systems. It focuses on identifying the location of several keypoints of the human faces in images or videos. Although several methods and models are available to developers in popular computer vision libraries, they still struggle with challenges such as insufficient illumination, extreme head poses, or occlusions, especially when they are constrained by the needs of real-time applications. Throughout this article, we propose a set of training strategies and implementations based on data augmentation, software optimization techniques that help in improving a large variety of models belonging to several real-time algorithms for face alignment. We propose an extended set of evaluation metrics that allow novel evaluations to mitigate the typical problems found in real-time tracking contexts. The experimental results show that the generated models using our proposed techniques are faster, smaller, more accurate, more robust in specific challenging conditions and smoother in tracking systems. In addition, the training strategy shows to be applicable across different types of devices and algorithms, making them versatile in both academic and industrial uses.