  Beneath the uncertain primitive visual features of face images are the
primitive intrinsic structural patterns (PISP) essential for characterizing a
sample face discriminative attributes. It is on this basis that this paper
presents a simple yet effective facial descriptor formed from derivatives of
Gaussian and Gabor Wavelets. The new descriptor is coined local edge gradient
Gabor magnitude (LEGGM) pattern. LEGGM first uncovers the PISP locked in every
pixel through determining the pixel gradient in relation to its neighbors using
the Derivatives of Gaussians. Then, the resulting output is embedded into the
global appearance of the face which are further processed using Gabor wavelets
in order to express its frequency characteristics. Additionally, we adopted
various subspace models for dimensionality reduction in order to ascertain the
best fit model for reporting a more effective representation of the LEGGM
patterns. The proposed descriptor-based face recognition method is evaluated on
three databases: Plastic surgery, LFW, and GT face databases. Through
experiments, using a base classifier, the efficacy of the proposed method is
demonstrated, especially in the case of plastic surgery database. The
heterogeneous database, which we created to typify real-world scenario, show
that the proposed method is to an extent insensitive to image formation factors
with impressive recognition performances.
