Abstract
Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data is available in advance. When new training samples arrive, the static model must be retrained from scratch, which is excessively time-consuming and memory-consuming. In many real-time applications, the training data is obtained one by one or batch by batch. It results in that the static model limits its performance on sequential images with extensive variations. Therefore, the most critical and challenging aspect in this field is dynamically updating the tracker's models to enhance predictive and generalization capabilities continuously. In order to address this question, we develop a fast and accurate online learning algorithm for face alignment. Particularly, we incorporate on-line sequential extreme learning machine into a parallel cascaded regression framework, coined incremental cascade regression(ICR). To the best of our knowledge, this is the first incremental cascaded framework with the non-linear regressor. One main advantage of ICR is that the tracker model can be fast updated in an incremental way without the entire retraining process when a new input is incoming. Experimental results demonstrate that the proposed ICR is more accurate and efficient on still or sequential images compared with the recent state-of-the-art cascade approaches. Furthermore, the incremental learning proposed in this paper can update the trained model in real time.
Abstract (translated)
传统的基于机器学习的面部定位通常使用离线培训的静态模型跟踪面部标志物的定位，其中所有的培训数据都提前可用。当新的训练样本到达时，静态模型必须从头开始重新训练，这是非常耗时和内存消耗的。在许多实时应用中，训练数据是逐批获取的。结果表明，静态模型限制了其在具有广泛变化的序列图像上的性能。因此，该领域最关键和最具挑战性的方面是动态更新跟踪器的模型，以持续增强预测和泛化能力。为了解决这一问题，我们开发了一种快速、准确的人脸对齐在线学习算法。特别是，我们将在线顺序极端学习机纳入并行级联回归框架，即创造的增量级联回归（ICR）。据我们所知，这是第一个使用非线性回归器的增量级联框架。ICR的一个主要优点是，当新输入进入时，跟踪器模型可以以增量方式快速更新，而无需整个再培训过程。实验结果表明，与目前最先进的级联方法相比，本文提出的ICR在静止图像或序列图像上更精确、更有效。此外，本文提出的增量学习可以实时更新训练模型。
URL
https://arxiv.org/abs/1905.04010
