Date: March 2021.
Source: EURASIP Journal on Image and Video Processing, Volume 2021, Article number: 9.
Abstract: 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.

Article: Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods.
Authors: Xiang Li, Jianzheng Liu, Jessica Baron, Khoa Luu, Eric Patterson. School of Computing, Clemson University, SC, USA.