  In this paper R-Theta Local Neighborhood Pattern (RTLNP) is proposed for
facial image retrieval. RTLNP exploits relationships amongst the pixels in
local neighborhood of the reference pixel at different angular and radial
widths. The proposed encoding scheme divides the local neighborhood into
sectors of equal angular width. These sectors are again divided into subsectors
of two radial widths. Average grayscales values of these two subsectors are
encoded to generate the micropatterns. Performance of the proposed descriptor
has been evaluated and results are compared with the state of the art
descriptors e.g. LBP, LTP, CSLBP, CSLTP, Sobel-LBP, LTCoP, LMeP, LDP, LTrP,
MBLBP, BRINT and SLBP. The most challenging facial constrained and
unconstrained databases, namely; AT&T, CARIA-Face-V5-Cropped, LFW, and Color
FERET have been used for showing the efficiency of the proposed descriptor.
Proposed descriptor is also tested on near infrared (NIR) face databases; CASIA
NIR-VIS 2.0 and PolyU-NIRFD to explore its potential with respect to NIR facial
images. Better retrieval rates of RTLNP as compared to the existing state of
the art descriptors show the effectiveness of the descriptor
