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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We show how a simple convolutional neural network (CNN) can be trained to accurately and robustly regress 6 degrees of freedom (6DoF) 3D head pose, directly from image intensities. 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
In this work, we propose a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection. Moreover, we show the robustness of our method to the large variance of image styles by comparing to a variant of our approach, in which the generative adversarial module is removed, and no style-aggregated images are used.
SOTA for Facial Landmark Detection on 300W
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients. We further offer a novel means of evaluating the accuracy of estimated expression coefficients: by measuring how well they capture facial emotions on the CK+ and EmotiW-17 emotion recognition benchmarks.
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. Not only is face detection an important pre-processing step in computer vision applications but also in computational psychology, behavioral imaging and other fields where researchers might not be initiated in computer vision frameworks and state-of-the-art detection applications.
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms. Instead of computing the model parameters using iterative optimization, the PCA is included in a deep neural network using a novel layer type.
Convolutional neural networks (CNNs) have become the most successful approach in many vision-related domains. 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. In order to overcome this issue, we propose in this paper a regularization scheme for training neural networks for these particular tasks using a multi-task framework.