Abstract 2D Face recognition has been extensively studied for decades and has reached
remarkable results in recent years. However, 2D Face recognition is sensitive to variations in …

Deep learning approaches achieve highly accurate face recognition by training the models
with huge face image datasets. Unlike 2D face image datasets, there is a lack of large 3D …

Deep learning approaches have achieved highly accurate face recognition by training the
models with very large face image datasets. Unlike the availability of large 2D face image …

Abstract 2D face recognition encounters challenges in unconstrained environments due to
varying illumination occlusion and pose. Recent studies focus on RGB-D face recognition to …

After decades of extensive research, the field of 2D face recognition is fruitful. However, 2D
face recognition is sensitive to changes in pose, facial expression and illumination. 3D face …

Due to the intrinsic invariance to pose and illumination changes, 3D Face Recognition (FR)
has a promising potential in the real world. 3D FR using high-quality faces, which are of high …

Deep networks trained on millions of facial images are believed to be closely approaching
human-level performance in face recognition. However, open world face recognition still …

Face recognition has been of great importance in many applications as a biometric for its
throughput, convenience, and non-invasiveness. Recent advancements in deep …

With the superiority of three-dimensional (3D) scanning data, eg, illumination invariance and
pose robustness, 3D face recognition theoretically has the potential to achieve better results …

Three-dimensional (3D) face recognition has been extensively investigated in the last two
decades due to its wide range of applications in many areas, such as security and forensics …