Enhancing Facial Recognition Accuracy through KNN Classification with Principal Component Analysis and Local Binary Pattern-Reference-Cited by-同舟云学术

Enhancing Facial Recognition Accuracy through KNN Classification with Principal Component Analysis and Local Binary Pattern

Published:2024-07-25 Issue:3 Volume:12 Page:791-798
ISSN:2347-470X
Container-title:International Journal of Electrical and Electronics Research
language:en
Short-container-title:IJEER

Author:

Gaur Sachin¹,Tiwari Nitesh¹,Vyas Sneha¹,Pandey Milind²

Affiliation:

1. Department of Computer Science and Engineering B.T Kumaon Institute of Technology Dwarahat, India

2. B. T Kumaon institute of Technology Dwarahat, India

Abstract

Recent developments in deep learning techniques have led to remarkable progress in facial recognition. As a component of biometric verification, human face recognition has become widely used in a variety of applications, including surveillance systems, home entry access, mobile face unlocking, and network security. Conventional facial recognition techniques are especially useful when dealing with low-resolution photos or difficult lighting situations. The K-nearest neighbor (KNN) classifier has been used in this paper. KNN is a non-parametric, instance-based learning algorithm that is commonly used for classification tasks. Principal Component Analysis (PCA) and local binary pattern (LBP) are used in this study to develop face identification. Both contrast stretching and grayscale were used to ensure ease of computation. The study was conducted on two separate and through multiple tests with different values for k, the highest accuracy obtained was at k=1 for both datasets. The smaller user dataset achieved 91% accuracy and CASIA-WebFace obtained 87% model accuracy.

Publisher

FOREX Publication

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