Abstract:
Traditional authentication methods, such as passwords and PINs, are susceptible to security breaches and unauthorized access. This project aims to develop a secure authentication system using biometric data, specifically focusing on biometric modalities such as fingerprints or facial recognition. By leveraging advanced biometric algorithms and machine learning techniques, the proposed system will authenticate users based on their unique biometric features, providing a more secure and convenient authentication process. The system’s accuracy, robustness, and resistance to spoofing attacks will be evaluated using a dataset of biometric samples, and its potential for integration into existing authentication systems will be explored.

Table of Contents:
Chapter 1: Introduction
1.1 Background and Motivation
1.2 Problem Statement
1.3 Objectives
1.4 Scope and Limitations
1.5 Methodology

Chapter 2: Literature Review
2.1 Overview of Authentication Systems
2.2 Biometric Modalities for Authentication
2.3 Biometric Data Acquisition and Preprocessing
2.4 Biometric Feature Extraction and Representation
2.5 Summary of Existing Research

Chapter 3: Data Collection and Preprocessing
3.1 Biometric Data Acquisition
3.2 Data Cleaning and Transformation
3.3 Feature Extraction and Selection
3.4 Data Splitting and Cross-Validation

Chapter 4: Biometric Authentication Model Development
4.1 Biometric Feature Extraction Algorithms
4.2 Machine Learning Algorithms for Biometric Authentication
4.3 Model Training and Optimization
4.4 Performance Evaluation and Validation

Chapter 5: System Implementation and Evaluation
5.1 System Architecture and Integration
5.2 Biometric Data Capture and Processing
5.3 Real-Time Authentication Testing
5.4 Performance Evaluation and Comparison
5.5 Security Analysis and Vulnerability Assessment

5.5 Conclusion and Future Work