TY - GEN
T1 - Fast and precise face alignment and 3D shape reconstruction from a single 2D image
AU - Zhao, Ruiqi
AU - Wang, Yan
AU - Fabian Benitez-Quiroz, C.
AU - Liu, Yaojie
AU - Martinez, Aleix M.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Many face recognition applications require a precise 3D reconstruction of the shape of the face, even when only a single 2D image is available. We present a novel regression approach that learns to detect facial landmark points and estimate their 3D shape rapidly and accurately from a single face image. The main idea is to regress a function f (.) that maps 2D images of faces to their corresponding 3D shape from a large number of sample face images under varying pose, illumination, identity and expression. To model the non-linearity of this function, we use a deep neural network and demonstrate how it can be efficiently trained using a large number of samples. During testing, our algorithm runs at more than 30 frames/s on an i7 desktop. This algorithm was the top 2 performer in the 3DFAW Challenge.
AB - Many face recognition applications require a precise 3D reconstruction of the shape of the face, even when only a single 2D image is available. We present a novel regression approach that learns to detect facial landmark points and estimate their 3D shape rapidly and accurately from a single face image. The main idea is to regress a function f (.) that maps 2D images of faces to their corresponding 3D shape from a large number of sample face images under varying pose, illumination, identity and expression. To model the non-linearity of this function, we use a deep neural network and demonstrate how it can be efficiently trained using a large number of samples. During testing, our algorithm runs at more than 30 frames/s on an i7 desktop. This algorithm was the top 2 performer in the 3DFAW Challenge.
KW - 3D modeling and reconstruction of faces
KW - 3D shape from a single 2D image
KW - Fine-grained detection
KW - Precise and detailed detections
UR - https://www.scopus.com/pages/publications/84996757691
U2 - 10.1007/978-3-319-48881-3_41
DO - 10.1007/978-3-319-48881-3_41
M3 - 会议稿件
AN - SCOPUS:84996757691
SN - 9783319488806
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 590
EP - 603
BT - Computer Vision – ECCV 2016 Workshops, Proceedings
A2 - Hua, Gang
A2 - Jegou, Herve
PB - Springer Verlag
T2 - Computer Vision - ECCV 2016 Workshops, Proceedings
Y2 - 8 October 2016 through 16 October 2016
ER -