TY - GEN
T1 - Towards 3D facial reconstruction using deep neural networks
AU - Munir, Hafiz Muhammad Umair
AU - Qureshi, Waqar S.
N1 - Publisher Copyright:
© Copyright 2019 IADIS Press. All rights reserved.
PY - 2019
Y1 - 2019
N2 - 3D facial reconstruction is an emerging and interesting application in the field of computer graphics and computer vision and is been used for 3D printing, creating avatars etc. It is difficult and challenging to reconstruct the 3D model of face from a single photo because of arbitrary poses, non-uniform illumination, expressions and occlusions. None of algorithm provides detailed 3D facial model because every algorithm has some limitations related to profile view, fine detail, accuracy and speed. The major problem is to develop 3D face with texture of large poses, wild faces and occluded faces. Mostly algorithm is used convolution neural network (CNN) and deep learning frameworks to create facial model and 3D dense face alignment (3DDFA) is the first algorithm that constructed the database consisting of 2D images and 3D facial model. In this paper we review 3D face reconstruction algorithm for application such as 3D printing, creating avatars and facial recognition. Different issues, problems and their proposed solutions are discussed in this paper while advantages and disadvantages are highlighted. A comparison of different algorithms is described in the context of texture and poses to find the best solution regarding reconstruction of 3D facial model from single photo.
AB - 3D facial reconstruction is an emerging and interesting application in the field of computer graphics and computer vision and is been used for 3D printing, creating avatars etc. It is difficult and challenging to reconstruct the 3D model of face from a single photo because of arbitrary poses, non-uniform illumination, expressions and occlusions. None of algorithm provides detailed 3D facial model because every algorithm has some limitations related to profile view, fine detail, accuracy and speed. The major problem is to develop 3D face with texture of large poses, wild faces and occluded faces. Mostly algorithm is used convolution neural network (CNN) and deep learning frameworks to create facial model and 3D dense face alignment (3DDFA) is the first algorithm that constructed the database consisting of 2D images and 3D facial model. In this paper we review 3D face reconstruction algorithm for application such as 3D printing, creating avatars and facial recognition. Different issues, problems and their proposed solutions are discussed in this paper while advantages and disadvantages are highlighted. A comparison of different algorithms is described in the context of texture and poses to find the best solution regarding reconstruction of 3D facial model from single photo.
KW - 3D Facial Model
KW - Computer Vision
KW - Convolution Neural Network
KW - Deep Learning Frameworks
UR - http://www.scopus.com/inward/record.url?scp=85073106855&partnerID=8YFLogxK
U2 - 10.33965/cgv2019_201906r066
DO - 10.33965/cgv2019_201906r066
M3 - Conference contribution
AN - SCOPUS:85073106855
T3 - Multi Conference on Computer Science and Information Systems, MCCSIS 2019 - Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2019, Game and Entertainment Technologies 2019 and Computer Graphics, Visualization, Computer Vision and Image Processing 2019
SP - 447
EP - 450
BT - Multi Conference on Computer Science and Information Systems, MCCSIS 2019 - Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2019, Game and Entertainment Technologies 2019 and Computer Graphics, Visualization, Computer Vision and Image Processing 2019
A2 - Blashki, Katherine
A2 - Xiao, Yingcai
A2 - Rodrigues, Luis
PB - IADIS Press
T2 - 13th International Conference on Interfaces and Human Computer Interaction 2019, IHCI 2019, 12th International Conference on Game and Entertainment Technologies 2019, GET 2019 and 13th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2019, CGVCVIP 2019
Y2 - 16 July 2019 through 18 July 2019
ER -