Facial landmark detection is the task of detecting key landmarks on the face and tracking them (being robust to rigid and non-rigid facial deformations due to head movements and facial expressions).
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Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies.
While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases.
To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder.
Landmark localization is a challenging problem in computer vision with a multitude of applications.
SOTA for Facial Landmark Detection on 300W
Furthermore, while CycleGAN uses two cycle consistency constraints, we show that the second one is detrimental in this application and we discard it, significantly simplifying the model.
Despite this, we notice that the semantic ambiguity greatly degrades the detection performance.
In this model, the occlusion probability of each position in high-level features are inferred by a distillation module that can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape.
We perform our evaluation not only on frontal faces but also on profile faces and in various regions of the face.
In the forensic field, the focus is on the analysis of a particular set of facial landmarks, defined as cephalometric landmarks.