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 are available in advance. When new training samples arrive, the static model must be retrained from scratch, which is excessively time-consuming and memory-consuming. It results in the limitation of its performance on sequential images with extensive variations. Therefore, the most critical and challenging aspect in this field is how to enhance the predictive capability of pretrained model incrementally. To that end, a fast and accurate online learning algorithm for face alignment is proposed. Particularly, extreme learning machine (ELM) is incorporated into a parallel cascaded regression (CR) framework, which we coin parallel cascade regression based on extreme learning machine (CRELM). The proposed model can be fast updated in an incremental way. It has a stronger prediction capability than conventional CR methods. The experimental results demonstrate that the proposed model is more accurate and efficient on still images or videos compared with the recent state-of-the-art approaches.

中文翻译：

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基于极限学习机的级联回归进行人脸对齐

摘要。基于机器学习的传统面部对齐通常使用离线训练的静态模型跟踪面部标志的定位，其中所有训练数据都提前可用。当新的训练样本到来时，静态模型必须从头开始重新训练，这非常耗时和内存消耗。这导致其在具有广泛变化的连续图像上的性能受到限制。因此，该领域最关键和最具挑战性的方面是如何逐步增强预训练模型的预测能力。为此，提出了一种快速准确的人脸对齐在线学习算法。特别是，极限学习机（ELM）被纳入并行级联回归（CR）框架，我们创造了基于极限学习机（CRELM）的并行级联回归。所提出的模型可以以增量方式快速更新。它比传统的CR方法具有更强的预测能力。实验结果表明，与最近的最先进方法相比，所提出的模型在静止图像或视频上更准确、更有效。