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
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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