  Face images captured in heterogeneous environments, e.g., sketches generated
by the artists or composite-generation software, photos taken by common cameras
and infrared images captured by corresponding infrared imaging devices, usually
subject to large texture (i.e., style) differences. This results in heavily
degraded performance of conventional face recognition methods in comparison
with the performance on images captured in homogeneous environments. In this
paper, we propose a novel sparse graphical representation based discriminant
analysis (SGR-DA) approach to address aforementioned face recognition in
heterogeneous scenarios. An adaptive sparse graphical representation scheme is
designed to represent heterogeneous face images, where a Markov networks model
is constructed to generate adaptive sparse vectors. To handle the complex
facial structure and further improve the discriminability, a spatial
partition-based discriminant analysis framework is presented to refine the
adaptive sparse vectors for face matching. We conducted experiments on six
commonly used heterogeneous face datasets and experimental results illustrate
that our proposed SGR-DA approach achieves superior performance in comparison
with state-of-the-art methods.
