3D face recognition (FR) is a popular topic in computer vision, since 3D face data is invariant
to pose and illumination condition changes which easily affect the performance of 2D FR …

Face recognition is one of the most studied research topics in the community. In recent
years, the research on face recognition has shifted to using 3D facial surfaces, as more …

In the past ten years, research on face recognition has shifted to using 3D facial surfaces, as
3D geometric information provides more discriminative features. This comprehensive survey …

Though 2D face recognition (FR) has achieved great success due to powerful 2D CNNs and
large-scale training data, it is still challenged by extreme poses and illumination conditions …

Benefiting from deep learning with large scale face databases, 2D face recognition has
made significant progress in recent years. However, it still highly depends on lighting …

Massive amount of training samples is a challenge for 3D face recognition using deep
learning frame. This paper shows a method that uses deep twin neural network for 3D face …

3D face recognition (FR) has become increasingly widespread due to the illumination
invariance and pose robustness of 3D face data. Most existing 3D FR methods can only …

With recent advances in RGB-D sensing technologies as well as improvements in machine
learning and fusion techniques, RGB-D facial recognition has become an active area of …

The accuracy of 2D face recognition (FR) has progressed significantly due to the availability
of large-scale training data. However, the research of deep learning based 3D FR is still in …

Face recognition remains a challenge today as recognition performance is strongly affected
by variability such as illumination, expressions and poses. In this work we apply …