  Facial features are defined as the local relationships that exist amongst the
pixels of a facial image. Hand-crafted descriptors identify the relationships
of the pixels in the local neighbourhood defined by the kernel. Kernel is a two
dimensional matrix which is moved across the facial image. Distinctive
information captured by the kernel with limited number of pixel achieves
satisfactory recognition and retrieval accuracies on facial images taken under
constrained environment (controlled variations in light, pose, expressions, and
background). To achieve similar accuracies under unconstrained environment
local neighbourhood has to be increased, in order to encode more pixels.
Increasing local neighbourhood also increases the feature length of the
descriptor. In this paper we propose a hand-crafted descriptor namely Centre
Symmetric Quadruple Pattern (CSQP), which is structurally symmetric and encodes
the facial asymmetry in quadruple space. The proposed descriptor efficiently
encodes larger neighbourhood with optimal number of binary bits. It has been
shown using average entropy, computed over feature images encoded with the
proposed descriptor, that the CSQP captures more meaningful information as
compared to state of the art descriptors. The retrieval and recognition
accuracies of the proposed descriptor has been compared with state of the art
hand-crafted descriptors (CSLBP, CSLTP, LDP, LBP, SLBP and LDGP) on bench mark
databases namely; LFW, Colour-FERET, and CASIA-face-v5. Result analysis shows
that the proposed descriptor performs well under controlled as well as
uncontrolled variations in pose, illumination, background and expressions.
