  This paper presents a new proposal of an efficient computational model of
face recognition which uses cues from the distributed face recognition
mechanism of the brain, and by gathering engineering equivalent of these cues
from existing literature. Three distinct and widely used features: Histogram of
Oriented Gradients (HOG), Local Binary Patterns (LBP), and Principal components
(PCs) extracted from target images are used in a manner which is simple, and
yet effective. The HOG and LBP features further undergo principal component
analysis for dimensionality reduction. Our model uses multi-layer perceptrons
(MLP) to classify these three features and fuse them at the decision level
using sum rule. A computational theory is first developed by using concepts
from the information processing mechanism of the brain. Extensive experiments
are carried out using ten publicly available datasets to validate our proposed
model's performance in recognizing faces with extreme variation of
illumination, pose angle, expression, and background. Results obtained are
extremely promising when compared with other face recognition algorithms
including CNN and deep learning-based methods. This highlights that simple
computational processes, if clubbed properly, can produce competing performance
with best algorithms.
