  Face sketch to digital image matching is an important challenge of face
recognition that involves matching across different domains. Current research
efforts have primarily focused on extracting domain invariant representations
or learning a mapping from one domain to the other. In this research, we
propose a novel transform learning based approach termed as DeepTransformer,
which learns a transformation and mapping function between the features of two
domains. The proposed formulation is independent of the input information and
can be applied with any existing learned or hand-crafted feature. Since the
mapping function is directional in nature, we propose two variants of
DeepTransformer: (i) semi-coupled and (ii) symmetrically-coupled deep transform
learning. This research also uses a novel IIIT-D Composite Sketch with Age
(CSA) variations database which contains sketch images of 150 subjects along
with age-separated digital photos. The performance of the proposed models is
evaluated on a novel application of sketch-to-sketch matching, along with
sketch-to-digital photo matching. Experimental results demonstrate the
robustness of the proposed models in comparison to existing state-of-the-art
sketch matching algorithms and a commercial face recognition system.
