FACIAL FEATURE EXTRACTION & APPEARANCE BASED HUMAN AFFECT RECOGNITION SYSTEM

Abstract

There has been enormous amount of research on face recognition which consists of modification of state of
art face recognition methods to make it able to work under partial occlusion, illumination/pose changes. Those
modifications came up with additional computations which have compromised with execution time for accuracy. The
modified version of LRC(linear regression classification) known as CRC (census regression based classification)
grabbed an attention of researchers through its recognition performance. These CRC generates census transformed test
and training images then estimates importance of each pixels which takes twice amount of time than execution time of
LRC. The goal of this research is to improve execution performance of CRC(census regression classification) by
eventually switch to LRC face recognition system for non-Face Matching face by detecting whether the face is Face
Matching or not prior to face recognition using Facial Feature Extraction of face and SVM. Experimental analysis
shows that proposed occlusion Recognition method has 96% accuracy for artificially generated occlusions of face
images of SQL face database and execution time has been reduced by more than 50% for non- Face Matching faces.