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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The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
SOTA for Face Identification on IJB-B
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Face detection is a very important task and a necessary pre-processing step for many applications such as facial landmark detection, pose estimation, sentiment analysis and face recognition.
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.
We show that CNNs connected with our Deep Collaboration obtain better accuracy on facial landmark detection with related tasks.
The motivation of this work is to learn the output dependencies that may lie in the output data in order to improve the prediction accuracy.