f Enhancing Facial Recognition Accuracy through KNN Classification with Principal Component Analysis and Local Binary Pattern
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Research Article |

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

Author(s): Sachin Gaur*, Nitesh Tiwari, Sneha Vyas and Milind Pandey

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 3

Publisher : FOREX Publication

Published : 25 July 2024

e-ISSN : 2347-470X

Page(s) : 791-798

DOI: https://doi.org/10.37391/IJEER.120309

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.

Keywords: Face Recognition, Haar Cascade, Principal Component Analysis, Local Binary Pattern, K-Nearest Neighbor.




Sachin Gaur*, Department of Computer Science and Engineering B.T Kumaon Institute of Technology Dwarahat, India; Email: ersgaur1234@gmail.com

Nitesh Tiwari, Department of Computer Science and Engineering B.T Kumaon Institute of Technology Dwarahat, India; Email: 2003niteshtiwari@gmail.com

Sneha Vyas, Department of Computer Science and Engineering B.T Kumaon Institute of Technology Dwarahat, India; Email: snehavyas2707@gmail.com;

Milind Pandey, B. T Kumaon institute of Technology Dwarahat, India; Email: m.p6092003@gmail.com;

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Sachin Gaur, Nitesh Tiwari and Sneha Vyas (2024), Enhancing Facial Recognition Accuracy through KNN Classification with Principal Component Analysis and Local Binary Pattern. IJEER 12(3), 791-798. DOI: 10.37391/IJEER.120309.