Continual Online Learning For Unconstrained Facial Landmark Detection And Tracking

人脸标志点定位在计算机视觉中具有重要意义，因为它在解决许多关键的情感计算问题，如人脸识别，表情识别，情感检测等方面至关重要。人脸标志点对齐，这是关于眼睛，鼻子，嘴巴等关键面部结构的定位，由于宽范围的面部外观变化，在本质上是相当具有挑战性的。这主要是由于各种头部姿势、环境照明条件、外部遮挡和成像传感器噪声模式。构建地标检测和跟踪解决方案的最重要挑战源于这样一个事实，即很难用完全跨越所有上述属性空间的训练数据来训练机器学习模型。因此，通过使模型能够从测试时间环境中增量学习，可以在很大程度上解决这一挑战。然而，增量在线学习通常会导致一种称为灾难性干扰的现象，即当模型学习新信息时，它开始忘记以前学习的信息。连续学习的方法被证明是能够有效地处理这种干扰的情况下，连接主义的学习程序。在这个建议中，采用连续学习的方法来解决灾难性的遗忘现象在地标检测和跟踪模型的增量训练的可能性进行了讨论。

Facial landmark localization is of profound interest in computer vision since it is critical in solving many key affective computing problems such as face recognition, expression recognition, emotion detection etc. Face landmark alignment, which is all about localization of key facial structures such as eyes, nose, mouth etc., is quite challenging in nature owing to wide range of face appearance variations. This is mainly due to various head poses, environmental lighting conditions, external occlusions, and imaging sensor noise patterns. Most important challenge in building the landmark detection and tracking solution stems from the fact that it is difficult to train a machine learning model with training data that fully spans all the aforementioned attribute space. Hence by enabling the model to learn incrementally from test time environment, this challenge can be addressed to a significant extent. However, incremental online learning often results in a phenomenon known as catastrophic interference i.e. when the model learns new information it starts forgetting the previously learned information. Continual learning approaches are demonstrated to be capable of handling this interference effectively in case of connectionist learning procedures. In this proposal the possibility of employing the continual learning methods to address the catastrophic forgetting phenomenon during incremental training of landmark detection and tracking models is discussed.