3D-aided 2D Face Recognition
Abstract
In this paper, we present a new method for bidirectional relighting for 3D-aided 2D face recognition under
large pose and illumination changes. During subject enrollment, we build
subject-specific 3D annotated models by using the subjects' raw 3D data and 2D
texture. During authentication, the probe 2D images are projected onto a
normalized image space using the subject-specific 3D model in the gallery. Then,
a bidirectional relighting algorithm and two similarity metrics (a
view-dependent complex wavelet structural similarity and a global similarity)
are employed to compare the gallery and probe. We tested our algorithms on the
UHDB11 and UHDB12 databases that contain 3D data with probe images under large
lighting and pose variations. The experimental results show the robustness of
our approach in recognizing faces in difficult situations.
large pose and illumination changes. During subject enrollment, we build
subject-specific 3D annotated models by using the subjects' raw 3D data and 2D
texture. During authentication, the probe 2D images are projected onto a
normalized image space using the subject-specific 3D model in the gallery. Then,
a bidirectional relighting algorithm and two similarity metrics (a
view-dependent complex wavelet structural similarity and a global similarity)
are employed to compare the gallery and probe. We tested our algorithms on the
UHDB11 and UHDB12 databases that contain 3D data with probe images under large
lighting and pose variations. The experimental results show the robustness of
our approach in recognizing faces in difficult situations.
