Face landmarks / Neural Networks and Deep Learning

DragGAN: Open Source Model for Manipulating GAN-Generated Images

6 July 2023

DragGAN: Open Source Model for Manipulating GAN-Generated Images

Researchers from the Max Planck Institute, MIT, and Google have introduced DragGAN, an innovative approach that allows for seamless manipulation of images generated using Generative Adversarial Networks (GANs). By leveraging…

Fake It Till You Make It: Microsoft synthetic face images dataset

9 October 2021

Fake It Till You Make It: Microsoft synthetic face images dataset

Microsoft has introduced a dataset of synthetic facial images Fake It Till You Make It. The dataset is aimed at pre-training facial recognition algorithms before being used in real-world scenarios.…

Pairwise Relational Network – New Method for Face Recognition

28 November 2018

Pairwise Relational Network face recognition

Pairwise Relational Network – New Method for Face Recognition

With the rapid progress of deep learning in the past few years, many computer vision problems have been tackled and solved with human or even beyond human performance. One of…

PIFR: Pose Invariant 3D Face Reconstruction

26 November 2018

PIFR: Pose Invariant 3D Face Reconstruction

3D face geometry needs to be recovered from 2D images in many real-world applications, including face recognition, face landmark detection, 3D emoticon animation etc. However, this task remains challenging especially…

Fooling Facial Recognition: Fast Method for Generating Adversarial Faces

2 October 2018

Fooling Facial Recognition: Fast Method for Generating Adversarial Faces

With the rapid progress and state-of-the-art performance in a wide range of tasks, deep learning based methods are in use in a large number of security-sensitive and critical applications. However,…

True Face Super-Resolution Upscaling with the Facial Component Heatmaps

1 October 2018

True Face Super-Resolution Upscaling with the Facial Component Heatmaps

The performance of the most facial analysis techniques relies on the resolution of the corresponding image. Face alignment or face identification is not going to work correctly when the resolution…

Learning 3D Face Morphable Model Out of 2D Images

5 September 2018

Learning 3D Face Morphable Model Out of 2D Images

The 3D Morphable Model (3DMM) is a statistical model of 3D facial shape and texture. 3D Morphable Models have various applications in many fields including computer vision, computer graphics, human…

Facial Surface and Texture Synthesis via GAN

3 September 2018

Facial Surface and Texture Synthesis via GAN

Deep networks can be extremely powerful and effective in answering complex questions. But it is also well-known that in order to train a really complex model, you’ll need lots and…

Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors

14 August 2018

Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors

Precise facial landmark detection lays the foundation for a high-quality performance of many computer vision and computer graphics tasks, such as face recognition, face animation and face reenactment. Many face…

State-of-the-Art Facial Expression Recognition Model: Introducing of Covariances

23 May 2018

State-of-the-Art Facial Expression Recognition Model: Introducing of Covariances

Recognizing facial expressions is quite an interesting and at the same time challenging task. The humans are likely to benefit significantly if facial expression recognition is performed automatically by computer…