Abstract:
A face recognition system is a computer application
for automatically identifying a person from a digital image.
Recognition of face in uncontrolled lightening situations is one of
the most important bottlenecks for practical face recognition
systems. This paper addresses the problem of illumination
effects on Face recognition and work for an approach to reduce
their effect on recognition performance. For this following
methods are used: (i) simple and efficient preprocessing chain
that eliminates most of the effects of changing illumination while
still preserving the essential appearance details that are needed
for recognition; (ii) Local Binary Pattern (LBP) texture
descriptor which labels the pixels of an image and gives output
as a histogram of image; and (iii) principle component analysis
(PCA) feature extraction algorithm is used to improve
robustness. The proposed method is tested on ORL face
database. The crux of the work lies in optimizing Euclidean
distance classifier for recognition of face.