YesThe most recent face recognition systems are
mainly dependent on feature representations obtained using
either local handcrafted-descriptors, such as local binary patterns
(LBP), or use a deep learning approach, such as deep
belief network (DBN). However, the former usually suffers
from the wide variations in face images, while the latter
usually discards the local facial features, which are proven
to be important for face recognition. In this paper, a novel
framework based on merging the advantages of the local
handcrafted feature descriptors with the DBN is proposed to
address the face recognition problem in unconstrained conditions.
Firstly, a novel multimodal local feature extraction
approach based on merging the advantages of the Curvelet
transform with Fractal dimension is proposed and termed
the Curvelet–Fractal approach. The main motivation of this
approach is that theCurvelet transform, a newanisotropic and
multidirectional transform, can efficiently represent themain
structure of the face (e.g., edges and curves), while the Fractal
dimension is one of the most powerful texture descriptors
for face images. Secondly, a novel framework is proposed,
termed the multimodal deep face recognition (MDFR)framework,
to add feature representations by training aDBNon top
of the local feature representations instead of the pixel intensity
representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary
to those acquired by the Curvelet–Fractal approach.
Finally, the performance of the proposed approaches has
been evaluated by conducting a number of extensive experiments
on four large-scale face datasets: the SDUMLA-HMT,
FERET, CAS-PEAL-R1, and LFW databases. The results
obtained from the proposed approaches outperform other
state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by
achieving new state-of-the-art results on all the employed
datasets
