  Face recognition system is one of the esteemed research areas in pattern
recognition and computer vision as long as its major challenges. A few
challenges in recognizing faces are blur, illumination, and varied expressions.
Blur is natural while taking photographs using cameras, mobile phones, etc.
Blur can be uniform and non-uniform. Usually non-uniform blur happens in images
taken using handheld image devices. Distinguishing or handling a blurred image
in a face recognition system is generally tough. Under varying lighting
conditions, it is challenging to identify the person correctly. Diversified
facial expressions such as happiness, sad, surprise, fear, anger changes or
deforms the faces from normal images. Identifying faces with facial expressions
is also a challenging task, due to the deformation caused by the facial
expressions. To solve these issues, a pre-processing step was carried out after
which Blur and Illumination-Robust Face recognition (BIRFR) algorithm was
performed. The test image and training images with facial expression are
transformed to neutral face using Facial expression removal (FER) peration.
Every training image is transformed based on the optimal Transformation Spread
Function (TSF), and illumination coefficients. Local Binary Pattern (LBP)
features extracted from test image and transformed training image is used for
classification.
