Fuzzy Based Face Recognition System using Machine Learning Algorithms
- Version
- Download 7
- File Size 653.18 KB
- File Count 1
- Create Date 16 June 2025
- Last Updated 16 June 2025
Fuzzy Based Face Recognition System using Machine Learning Algorithms
Authors:
- K Nikitha1, A. Aasritha2, Ch. Nagavardhan3, B. Vamsi Naik4
Undergraduate students, Department of ECE, R.V. R. & J. C. College of Engineering, Chowdavaram, Guntur,
A.P., India1-4
ABSTRACT: Face recognition is a hard computer vision problem with increasingly more applications in finance, healthcare, and security. Yet, being insensitive to varying illumination, facial expressions, poses, and occlusions is still a hard problem. Current systems are non-robust and generalize poorly due to poor feature extraction and inefficient classifier performance. Experiments have to be performed with the implementation of strong feature extraction methods and high-performance classifiers to enhance system performance. Hybrid methods using the combination of various features may possess potential for accomplishing higher recognition accuracy and reliability. This paper provides a comparative analysis of ANN, SVM, and RF classifiers using HOG, fuzzy FIS, and hybrid combinations of features for face recognition. The proposed model indicates that fuzzy-based feature extraction and hybrid features improve the system's robustness, accuracy, and reliability when tested on the ORL dataset.
KEYWORDS: Face Recognition, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), Histogram of Oriented Gradients (HOG), Fuzzy Inference System (FIS), Hybrid Feature Extraction.
Download