Τίτλος:
3D-2D face recognition with pose and illumination normalization
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
In this paper, we propose a 3D-2D framework for face recognition that is more practical than 3D-3D, yet more accurate than 2D-2D. For 3D-2D face recognition, the gallery data comprises of 3D shape and 2D texture data and the probes are arbitrary 2D images. A 3D-2D system (UR2D) is presented that is based on a 3D deformable face model that allows registration of 3D and 2D data, face alignment, and normalization of pose and illumination. During enrollment, subject-specific 3D models are constructed using 3D+2D data. For recognition, 2D images are represented in a normalized image space using the gallery 3D models and landmark-based 3D-2D projection estimation. A method for bidirectional relighting is applied for non-linear, local illumination normalization between probe and gallery textures, and a global orientation-based correlation metric is used for pairwise similarity scoring. The generated, personalized, pose- and light- normalized signatures can be used for one-to-one verification or one-to-many identification. Results for 3D-2D face recognition on the UHDB11 3D-2D database with 2D images under large illumination and pose variations support our hypothesis that, in challenging datasets, 3D-2D outperforms 2D-2D and decreases the performance gap against 3D-3D face recognition. Evaluations on FRGC v2.0 3D-2D data with frontal facial images, demonstrate that the method can generalize to databases with different and diverse illumination conditions. © 2016
Συγγραφείς:
Kakadiaris, I.A.
Toderici, G.
Evangelopoulos, G.
Passalis, G.
Chu, D.
Zhao, X.
Shah, S.K.
Theoharis, T.
Περιοδικό:
Computer Vision and Image Understanding
Εκδότης:
Academic Press Inc.
Λέξεις-κλειδιά:
Biometrics; Computer vision; Gesture recognition; Large dataset; Object recognition; Probes; Textures, Face and gesture recognition; Illumination normalization; Model fitting; Model-based OPC; Physically based modeling, Face recognition
DOI:
10.1016/j.cviu.2016.04.012
