论文标题
详细介绍了面部地标检测中基于CNN的方法
A Detailed Look At CNN-based Approaches In Facial Landmark Detection
论文作者
论文摘要
几十年来，已经研究了面部地标检测。已经提出了许多基于神经网络（NN）的方法来检测地标，尤其是基于卷积神经网络（CNN）的方法。通常，基于CNN的方法可以分为回归和热图方法。但是，没有研究系统地研究不同方法的特征。在本文中，我们研究了这两种基于CNN的方法，概括它们的优势和缺点，并引入了Heatmap方法的变体，即像素分类（PWC）模型。据我们所知，使用PWC模型检测面部地标尚未进行全面研究。我们进一步设计了一个混合损失函数和一个歧视网络，以加强PWC模型中隐含地标的相互关系，以提高检测准确性而无需修改原始模型体系结构。采用六个常见的面部标志性数据集，即AFW，Helen，LFPW，300-W，IBUG和COFW进行培训或评估我们的模型。进行了全面的评估，结果表明，所提出的模型在所有测试的数据集中都优于其他模型。
Facial landmark detection has been studied over decades. Numerous neural network (NN)-based approaches have been proposed for detecting landmarks, especially the convolutional neural network (CNN)-based approaches. In general, CNN-based approaches can be divided into regression and heatmap approaches. However, no research systematically studies the characteristics of different approaches. In this paper, we investigate both CNN-based approaches, generalize their advantages and disadvantages, and introduce a variation of the heatmap approach, a pixel-wise classification (PWC) model. To the best of our knowledge, using the PWC model to detect facial landmarks have not been comprehensively studied. We further design a hybrid loss function and a discrimination network for strengthening the landmarks' interrelationship implied in the PWC model to improve the detection accuracy without modifying the original model architecture. Six common facial landmark datasets, AFW, Helen, LFPW, 300-W, IBUG, and COFW are adopted to train or evaluate our model. A comprehensive evaluation is conducted and the result shows that the proposed model outperforms other models in all tested datasets.
