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Laplace Landmark Localization

2019-03-27 18:14:50
Joseph P Robinson, Yuncheng Li, Ning Zhang, Yun Fu, and Sergey Tulyakov

Abstract

Landmark localization in images and videos is a classic problem solved in various ways. Nowadays, with deep networks prevailing throughout machine learning, there are revamped interests in pushing facial landmark detection technologies to handle more challenging data. Most efforts use network objectives based on L1 or L2 norms, which have several disadvantages. First of all, the locations of landmarks are determined from generated heatmaps (i.e., confidence maps) from which predicted landmark locations (i.e., the means) get penalized without accounting for the spread: a high scatter corresponds to low confidence and vice-versa. For this, we introduce a LaplaceKL objective that penalizes for a low confidence. Another issue is a dependency on labeled data, which are expensive to obtain and susceptible to error. To address both issues we propose an adversarial training framework that leverages unlabeled data to improve model performance. Our method claims state-of-the-art on all of the 300W benchmarks and ranks second-to-best on the Annotated Facial Landmarks in the Wild (AFLW) dataset. Furthermore, our model is robust with a reduced size: 1/8 the number of channels (i.e., 0.0398MB) is comparable to state-of-that-art in real-time on CPU. Thus, we show that our method is of high practical value to real-life application.

Abstract (translated)

图像和视频中的地标定位是一个以各种方式解决的经典问题。如今,随着深层网络在整个机器学习中的流行,人们对推动面部标志性检测技术处理更具挑战性的数据的兴趣有所改观。大多数工作使用基于L1或L2规范的网络目标,这有几个缺点。首先,根据生成的热图(即信心图)确定地标的位置,从中预测的地标位置(即平均值)得到惩罚,而不考虑分布:高散点对应低信心,反之亦然。为此,我们引入了一个Laplacekl目标,即对低置信度进行惩罚。另一个问题是对标记数据的依赖性,这些数据获取起来很昂贵,而且容易出错。为了解决这两个问题,我们提出了一个对抗性的培训框架,利用未标记的数据来提高模型性能。我们的方法声称在所有300W基准上都是最先进的,并且在野外(AFLW)数据集中的带注释的面部标志上排名第二。此外,我们的模型具有强大的鲁棒性和较小的尺寸:1/8通道数量(即0.0398MB)与CPU上的实时技术状态相当。由此可见,该方法对实际应用具有很高的实用价值。

URL

https://arxiv.org/abs/1903.11633

PDF

https://arxiv.org/pdf/1903.11633.pdf


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