ArcFace: Additive Angular Margin Loss for Deep Face Recognition.

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains K sub-centers and training samples only need to be close to any of the K positive sub-centers.

Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction.

A lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of deep convolutional neural networks (DCNNs). In the recent works, the texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruction is still not capable of modeling facial texture with high-frequency details.

Guest editorial: Introduction to the special issue on modern control for computer games.

A typical gaming scenario, as developed in the past 20 years, involves a player interacting with a game using a specialized input device, such as a joystic, a mouse, a keyboard, etc. Recent technological advances and new sensors (for example, low cost commodity depth cameras) have enabled the introduction of more elaborated approaches in which the player is now able to interact with the game using his body pose, facial expressions, actions, and even his physiological signals. A new era of games has already started, employing computer vision techniques, brain-computer interfaces systems, haptic and wearable devices.

Tensor learning for regression.

In this paper, we exploit the advantages of tensorial representations and propose several tensor learning models for regression. The model is based on the canonical/parallel-factor decomposition of tensors of multiple modes and allows the simultaneous projections of an input tensor to more than one direction along each mode. Two empirical risk functions are studied, namely, the square loss and ε -insensitive loss functions.

Novel multiclass classifiers based on the minimization of the within-class variance.

In this paper, a novel class of multiclass classifiers inspired by the optimization of Fisher discriminant ratio and the support vector machine (SVM) formulation is introduced. The optimization problem of the so-called minimum within-class variance multiclass classifiers (MWCVMC) is formulated and solved in arbitrary Hilbert spaces, defined by Mercer's kernels, in order to find multiclass decision hyperplanes/surfaces. Afterwards, MWCVMCs are solved using indefinite kernels and dissimilarity measures via pseudo-Euclidean embedding.

Facial expression recognition in image sequences using geometric deformation features and Support Vector Machines.

In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity.