Bio metric authentication is becoming popular now a days and becoming
integral part of IoT and other systems. Face recognition is one of the major and important aspect of
bio metric systems after the fingerprint.
A face recognition algorithm with feature dimensionality reduction is proposed which is
very much required in recognition system for high speed and accuracy.
The proposed algorithm is based on a variant of Local Binary Pattern (LBP) for face detection
and recognition. The features of each block of face image is extracted and then global feature
of face is constructed from super histogram.
For recognition, traditional methods are used. The query image is compared with the data
set (ORL Dataset, LFW Dataset and Yale Dataset) in similarity index and the minimum distance.
The maximum similarity is used to define as the class of query image. The reduction in number of
features is achieved by modifying the traditional LBP process.
The proposed modified method is observed as more fast and efficient for face recognition
as compared to the existing algorithms.