RECOGNITION OF MULTI-VIEW HUMAN FACES BASED ON MACHINE INTELLIGENCE USING KLT ALGORITHM

Haripriya K; Ramya Lakshmi V.; Rajeswari S; Rama T; Vinothini K.R

doi:10.29121/granthaalayah.v5.i3.2017.1759

Authors

Haripriya.K Department of Electronics and Communication Engineering, A.V.C College of Engineering (Anna University), Mayiladuthurai, Tamilnadu, India
Ramya Lakshmi.V. Department of Electronics and Communication Engineering, A.V.C College of Engineering (Anna University), Mayiladuthurai, Tamilnadu, India
Rajeswari.S Department of Electronics and Communication Engineering, A.V.C College of Engineering (Anna University), Mayiladuthurai, Tamilnadu, India
Rama.T Department of Electronics and Communication Engineering, A.V.C College of Engineering (Anna University), Mayiladuthurai, Tamilnadu, India
Vinothini.K.R Assistant Professor, Department of Electronics and Communication Engineering, A.V.C College of Engineering (Anna University), Mayiladuthurai, Tamilnadu, India

DOI:

https://doi.org/10.29121/granthaalayah.v5.i3.2017.1759

Keywords:

Face Recognition, Feature Extraction, KLT Algorithm, Local Binary Pattern

Abstract [English]

Nowadays Image Processing has become a proficient domain due to the prolific techniques like face detection and face recognition. They play an important role in our society due to their use in wide range of applications such as surveillance, security, banking, and multimedia. One of major challenges faced in this technique of face recognition is difficulty in handling arbitrary pose variations in three dimensional representations. In video retrieval system, many approaches have been developed for recognition across pose variations and to assume the face poses to be known. These constraints made it semi-automatic. In this paper we propose a fully automatic method for multi-view face recognition of improving the accuracy or efficiency using local binary patterns. It uses tree-based data structure to create sub-grids. In this system we use KLT algorithm to detect and extract features automatically by using Eigen vectors and estimation of hessian value.

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