Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Li, X., Liu, J., Baron, J., Luu, K., & Patterson, E. (2021, December 1). Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods. Eurasip Journal on Image and Video Processing. Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1186/s13640-021-00549-3

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free