The goals of this paper are: (1) to enhance the quality of images of faces, (2) to enable 3D Morphable Models (3DMMs) to cope with severely degraded images, and (3) to reconstruct textured 3D faces with details
A face recognition demo performed by feeding images of faces recorded by a webcam into a trained FaceNet network to determine the identity of the face Resources Readme Activity Stars 383 stars Watchers 21 watching Forks 290 forks Report repository Releases No releases published Packages...
a training set of images of faces, whose gender male/female is obviously known. Of course, training and testing sets have no intersection. During matching, each pixel in the feature image F contributes to the calculation of the final score by voting by its own partial score s(x, y). ...
Given that visually evident and easily recognizable patterns of human facial characteristics co-vary with genomic ancestry, and based on the integration of three different sources of genome data, we generate average 3D faces to illustrate genomic ancestry variations within the 1,000 Genome project and...
CompareFacesuses machine learning algorithms, which are probabilistic. A false negative is an incorrect prediction that a face in the target image has a low similarity confidence score when compared to the face in the source image. To reduce the probability of false negatives, we recommend that ...
Compounding this dif- ficulty is the variability in CFS potency when attempting to suppress certain categories of target stimuli. For exam- ple, some studies (Moors et al., 2016; Yang et al., 2007) have found that suppression durations for images of faces tend to be relatively brief ...
A visual editor for manually annotating facial landmarks in images of human faces. - luigivieira/Facial-Landmarks-Annotation-Tool
This topic shows how to use the FaceDetector to detect faces in an image. The FaceTracker is optimized for tracking faces over time in a sequence of video frames.
Smile Vector is a Twitter bot that canmake any celebrity smile. It scrapes the web for pictures of faces, and then it morphs their expressions using a deep-learning-powered neural network. Its results aren’t perfect, but they’re created completely automatically, and it’s just a small hin...