Recent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

最近对面部对齐和界标检测方法的关注，特别是在深度卷积神经网络的应用中，取得了显着的进步。但是，这些神经网络和更传统的方法都没有针对因相机镜头焦距或受检者在整个观察半球上系统地观察视角而导致的性能差异进行过直接测试。这项工作使用具有变化的参数和相应的地面真实地标的逼真，合成的面部图像，可以比较对齐和界标检测技术相对于一般性能，跨焦距性能和跨视角的性能。在这些方面，对最近发布的高性能方法以及传统技术进行了比较。