Random Forests for Real Time 3D Face Analysis

Real-Time Speech-Driven 3D Face Animation

In this paper, we present an approach for real-time speech-driven 3D face animation using neural networks. We first analyze a 3D facial movement sequence of a talking subject and learn a quantitative representation of the facial deformations, called the 3D Motion Units (MUs). A 3D facial deformation can be approximated by a linear combination of the MUs weighted by the MU parameters (MUPs) – th...

Real-Time Head Pose Estimation Using Random Regression Forests

Automatic head pose estimation is useful in human computer interaction and biometric recognition. However, it is a very challenging problem. To achieve robust for head pose estimation, a novel method based on depth images is proposed in this paper. The bilateral symmetry of face is utilized to design a discriminative integral slice feature, which is presented as a 3D vector from the geometric c...

Real-Time 3D Face Acquisition Using Reconfigurable Hybrid Architecture

Acquiring 3D data of human face is a general problem which can be applied in face recognition, virtual reality, and many other applications. It can be solved using stereovision. This technique consistes in acquiring data in three dimensions from two cameras. The aim is to implement an algorithmic chain which makes it possible to obtain a three-dimensional space from two two-dimensional spaces: ...

Detection of Congenital Mullerian Anomalies Using Real-Time 3D Sonography

Combining Stacked Denoising Autoencoders and Random Forests for Face Detection

Detecting faces in the wild is a challenging problem due to large visual variations introduced by uncontrolled facial expressions, head pose, illumination and so on. Employing strong classifier and designing more discriminative visual features are two main approaches to overcoming such difficulties. Notably, Deep Neural Network (DNN) based methods have been found to outperform most traditional ...