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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.
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