Results 1 to 10 of about 5,138,121 (288)

Face ShapeNets for 3D Face Recognition

IEEE Access, 2023
In this paper, we present a deep learning-based method for 3D face recognition. Unlike some previous works, our process does not rely on face representation methods as a proxy step to be accepted by Convolutional Neural Networks (CNNs).
Marwa Jabberi, Ali Wali, Bilel Neji, Taha Beyrouthy, Adel M. Alimi   +4 more
doaj   +3 more sources

Random-Profiles-Based 3D Face Recognition System [PDF]

Sensors, 2014
In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain
Joongrock Kim, Sunjin Yu, Sangyoun Lee
doaj   +2 more sources

3D face recognition based on multiple keypoint descriptors and sparse representation. [PDF]

PLoS ONE, 2014
Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions.
Lin Zhang, Zhixuan Ding, Hongyu Li, Ying Shen, Jianwei Lu   +4 more
doaj   +2 more sources

Enhancing 3D Face Recognition: Achieving Significant Gains via 2D-Aided Generative Augmentation [PDF]

Sensors
The development of deep learning-based 3D face recognition has been constrained by the limited availability of large-scale 3D facial datasets, which are costly and labor-intensive to acquire.
Cuican Yu, Zihui Zhang, Huibin Li, Chang Liu   +3 more
doaj   +2 more sources

Intelligent multimodal 3D biometric recognition using PointNet + + for robust face–ear authentication [PDF]

Scientific Reports
In real-world applications, the reliability of biometric recognition systems that are based on 2D modalities is typically reduced due to limitations such as sensitivity to changes in illumination, facial expressions, and occlusion among other things.
Veerpal Kaur, Devershi Pallavi Bhatt, Sumegh Tharewal, Pradeep Kumar Tiwari   +3 more
doaj   +2 more sources

3D texture-based face recognition system using fine-tuned deep residual networks [PDF]

PeerJ Computer Science, 2019
As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition.
Siming Zheng, Rahmita Wirza OK Rahmat, Fatimah Khalid, Nurul Amelina Nasharuddin   +3 more
doaj   +2 more sources

Dense and Sparse 3D Deformation Signatures for 3D Dynamic Face Recognition

IEEE Access, 2021
This work analyses dense and sparse 3D Deformation Signatures to represent 3D temporal deformation instances. The signatures are employed in dynamic 3D face recognition, however, they are applicable in other domains.
Abd El Rahman Shabayek, Djamila Aouada
doaj   +1 more source

3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss

IET Biometrics, 2021
Restoration of a 3D face from the mesh image is highly demanded in computer vision applications. 3D face restoration is a challenging task due to the variation of expression, poses, intrinsic geometries, and textures.
Sahil Sharma, Vijay Kumar
doaj   +1 more source

A 3D Occlusion Facial Recognition Network Based on a Multi-Feature Combination Threshold

Applied Sciences, 2023
In this work, we propose a 3D occlusion facial recognition network based on a multi-feature combination threshold (MFCT-3DOFRNet). First, we design and extract the depth information of the 3D face point cloud, the elevation, and the azimuth angle of the ...
Kaifeng Zhu, Xin He, Zhuang Lv, Xin Zhang, Ruidong Hao, Xu He, Jun Wang, Jiawei He, Lei Zhang, Zhiya Mu   +9 more
doaj   +1 more source

A 3D-convolutional neural network framework with ensemble learning techniques for multi-modal emotion recognition

Egyptian Informatics Journal, 2021
Nowadays, human emotion recognition is a mandatory task for many human machine interaction fields. This paper proposes a novel multi-modal human emotion recognition framework. The proposed scheme utilizes first the 3D-Convolutional Neural Network (3D-CNN)
Elham S. Salama, Reda A. El-Khoribi, Mahmoud E. Shoman, Mohamed A. Wahby Shalaby   +3 more
doaj   +1 more source