Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 03, March 2024

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

Performance Analysis of Feature Extraction Techniques: PCA and LDA for Face Recognition

Author : N. Santhi 1 K. Annbuselvi 2 Dr. S. Sivakumar 3

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Date of Publication :30th March 2018

Abstract: Feature extraction is one of the most important steps in image pattern recognition. Some sources of difficulty are the presence of irrelevant information and the relativity of a feature set to a particular application. Feature extraction and description are essential components of various computer vision applications. The concept of feature extraction and description refers to the process of identifying points in an image (interesting points) that can be used to describe the image’s contents. The One major goal of feature extraction is to increase the accuracy of learned models by compactly extracting prominent features from the input data, while also possibly removing noise and redundancy from the input. Additional objectives include low-dimensional representations for data imagining and compression for the purpose of reducing data storage requirements as well as increasing training and implication speed. The aim of this paper is to report the result analysis of the most popular feature extraction techniques PCA and LDA using MATLAB to extract face features which are generally used in human recognition

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