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Research Article

Year : 2018 | Volume: 2 | Issue: 2 | Pages: 11-17

Study and Analysis of Differnet Pose Invarient for Face Recognition Under Lighting Condition

Rohit Raja1*, Md Rashid Mahmood Raj Kumar Patra2

doi:10.24951/sreyasijst.org/2018021003

Corresponding author

Rohit Raja*

Department of CSE, Sreyas Institute of Engineering and Technology, Nagole, Hyderabad, India

  • 1. Department of CSE, Sreyas Institute of Engineering and Technology, Nagole, Hyderabad, India
  • 2. Department of CSE, CMR Technical campus, Kandlakoya (v) Medchal Road, Hyderabad, India

Received on: 1/6/2019

Revised on: 1/6/2019

Accepted on: 1/6/2019

Published on: 1/6/2019

  • Study and Analysis of Differnet Pose Invarient for Face Recognition Under Lighting Condition, Rohit Raja, Md Rashid Mahmood Raj Kumar Patra., 1/6/2019, SREYAS International Journal of Scientists and Technocrats, 2(2), 11-17, http://dx.doi.org/10.18831/sreyasijst/12018021003.

    Published on: 1/6/2019

Abstract

Human face detection and recognition is the most influential area of image processing and analysis. It is one of the most manifold techniques used to distinguish an individual. There are two major challenges, Pose and Illumination among the various factors that impact the face recognition technique. The key objective of this paper is to develop a system which provides more precise face recognition system and recognizes the identity of a person with accuracy. The proposed system basically consists of two phases, image illumination and classifi- cation. Image illumination enhances the quality of image for the post phase of face recognition. Pose variations diminish the performance of human face recognition. Feature Extraction is the technique used to improve Performance and Dimensionality using Face Component Analysis and Discriminant Analysis. We propose a novel approach for face recognition under pose invariant and ambient illumination condition. Moreover, there will be no limitation on the invariant pose conditions. In the classification phase, images that were not considered in a training set, can be considered for testing. In order to train during the face recognition phase, various classifiers such as Naive Bayes Classifier and SVM (Support Vector Machine) algorithms are used to classify the images and analyze the face Recognition Rate.

Keywords

Bayesian Discriminating, Feature, Generalized, Discriminate Analysis, Support Vector Machines.