Sub-pixel face landmarks using heatmaps and a bag of tricks
- URL: http://arxiv.org/abs/2103.03059v2
- Date: Mon, 8 Mar 2021 02:55:39 GMT
- Title: Sub-pixel face landmarks using heatmaps and a bag of tricks
- Authors: Samuel W. F. Earp and Aubin Samacoits and Sanjana Jain and Pavit
Noinongyao and Siwa Boonpunmongkol
- Abstract summary: We present our heatmap regression approach to accurately localize face landmarks.
We use five na"ive face landmarks from a publicly available face detector to position and align the face.
We also show that it is possible to reduce the upscaling complexity by using a mixture of deconvolution and pixel-shuffle layers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate face landmark localization is an essential part of face recognition,
reconstruction and morphing. To accurately localize face landmarks, we present
our heatmap regression approach. Each model consists of a MobileNetV2 backbone
followed by several upscaling layers, with different tricks to optimize both
performance and inference cost. We use five na\"ive face landmarks from a
publicly available face detector to position and align the face instead of
using the bounding box like traditional methods. Moreover, we show by adding
random rotation, displacement and scaling -- after alignment -- that the model
is more sensitive to the face position than orientation. We also show that it
is possible to reduce the upscaling complexity by using a mixture of
deconvolution and pixel-shuffle layers without impeding localization
performance. We present our state-of-the-art face landmark localization model
(ranking second on The 2nd Grand Challenge of 106-Point Facial Landmark
Localization validation set). Finally, we test the effect on face recognition
using these landmarks, using a publicly available model and benchmarks.
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