  This paper reports a face identification system which makes use of a novel
local descriptor called Local Ternary Tree Pattern (LTTP). Exploiting and
extracting distinctive local descriptor from a face image plays a crucial role
in face identification task in the presence of a variety of face images
including constrained, unconstrained and plastic surgery images. LTTP has been
used to extract robust and useful spatial features which use to describe the
various structural components on a face. To extract the features, a ternary
tree is formed for each pixel with its eight neighbors in each block. LTTP
pattern can be generated in four forms such as LTTP Left Depth (LTTP LD), LTTP
Left Breadth (LTTP LB), LTTP Right Depth (LTTP RD) and LTTP Right Breadth (LTTP
RB). The encoding schemes of these patterns are very simple and efficient in
terms of computational as well as time complexity. The proposed face
identification system is tested on six face databases, namely, the UMIST, the
JAFFE, the extended Yale face B, the Plastic Surgery, the LFW and the UFI. The
experimental evaluation demonstrates the most promising results considering a
variety of faces captured under different environments. The proposed LTTP based
system is also compared with some local descriptors under identical conditions.
