Face Recognition for Real Time Application

Table of Contents

• CHAPTER 1: INTRODUCTION
• 1.1 Background
• 1.2 Motivation & Objective
• 1.2.1 Motivation
• 1.2.2 Problem Statement
• 1.2.3 Objective
• 1.3 Software Used
• 1.3.1 Open-CV
• 1.4 Database
• 1.4.1 Olivetti - Att - ORL [40]
• 1.4.2 The FERET Database, USA
• 1.5 Scope of Thesis
• 1.6 Organization of Thesis
• CHAPTER 2: FACE RECOGNITION SYSTEM
• 2.1 Face Recognition System
• 2.1.1 Face Recognition System Classification
• 2.1.2 Parameters of Face Recognition System
• 2.2 Real Time Face Recognition System
• 2.3 Real Time Face Recognition Model
• 2.3.1 Face Detection
• 2.3.2 Face Preprocessing
• 2.3.3 Feature Extraction
• 2.3.4 Feature Matching
• 2.4 Face Recognition Task
• 2.5 Dimension Reduction Technique Used
• 2.6 Problem & Challenges faced by Face Recognition System
• 2.7 Applications of Face Recognition System
• 2.7.1 Government Use
• 2.7.2 Commercial Use
• CHAPTER 3: LITERATURE SURVEY
• 3.1 Introduction
• 3.2 Literature Review
• 3.2.1 Comparison between Dimension Reduction Techniques
• 3.2.2 Summary of various papers

Objectives and Key Themes

This thesis aims to explore and implement a real-time face recognition system. The research investigates efficient methods for face detection, preprocessing, feature extraction, and matching, focusing on the application of Local Binary Patterns (LBP) for its robustness and computational efficiency. The study also considers the challenges and applications of such systems.

• Real-time face recognition system implementation
• Efficiency and robustness of Local Binary Patterns (LBP) in face recognition
• Challenges and limitations of face recognition technology
• Applications of face recognition in governmental and commercial sectors
• Comparative analysis of dimension reduction techniques

Chapter Summaries

CHAPTER 1: INTRODUCTION: This chapter provides background information on face recognition, its applications, and the motivation behind the research. It outlines the objectives of the thesis, including the problem statement and the specific goals. The chapter also details the software (OpenCV) and databases (Olivetti, FERET) used in the study, defining the scope and organizational structure of the thesis. It sets the stage for the subsequent chapters by introducing the key concepts and methodologies.

CHAPTER 2: FACE RECOGNITION SYSTEM: This chapter delves into the specifics of a face recognition system. It classifies different types of face recognition systems and explores the critical parameters influencing their performance. The main focus lies on real-time face recognition, detailing a model comprising face detection, preprocessing, feature extraction (using LBP), and feature matching. This chapter discusses the challenges faced in developing robust and efficient face recognition systems, along with its various applications in government and commercial settings, providing a comprehensive overview of the technological landscape.

CHAPTER 3: LITERATURE SURVEY: This chapter presents a thorough review of existing literature related to face recognition. It compares different dimension reduction techniques used in the field, analyzing their strengths and weaknesses. The chapter summarizes key findings and approaches from various research papers, offering a critical assessment of the current state-of-the-art in face recognition technology. This review serves as a foundation for the thesis's contributions and provides a context for evaluating the results obtained in subsequent chapters (if any).

Keywords

Face recognition, real-time processing, Local Binary Patterns (LBP), feature extraction, face detection, dimension reduction, OpenCV, Olivetti database, FERET database, biometrics, image processing, computer vision.

Frequently Asked Questions: Comprehensive Language Preview of Face Recognition Thesis

What is the main topic of this thesis?

This thesis focuses on the implementation and analysis of a real-time face recognition system. It investigates efficient methods for face detection, preprocessing, feature extraction, and matching, with a particular emphasis on the use of Local Binary Patterns (LBP).

What are the key objectives of the research?

The primary objective is to develop and implement a functional real-time face recognition system. The research also aims to evaluate the efficiency and robustness of LBP in face recognition, analyze the challenges and limitations of such systems, explore their applications in government and commercial sectors, and compare different dimension reduction techniques.

What methodology is used in this research?

The research utilizes Local Binary Patterns (LBP) for feature extraction due to its robustness and computational efficiency. OpenCV is employed as the software platform. The Olivetti and FERET databases are used for testing and evaluation. The methodology includes face detection, preprocessing, feature extraction, and feature matching stages within the real-time system.

What databases are used in this study?

The thesis uses two well-known face databases: the Olivetti-Att-ORL database and the FERET database.

What software is used in this research?

OpenCV is the primary software used for implementing and testing the face recognition system.

What are the key challenges addressed in the thesis?

The thesis addresses challenges related to developing a robust and efficient real-time face recognition system, including effective face detection, preprocessing, and feature extraction in real-world conditions. It also acknowledges limitations inherent in current face recognition technology.

What are the potential applications of the research?

The applications explored include governmental and commercial uses of face recognition technology, such as security systems, access control, and identification processes.

What is the structure of the thesis?

The thesis is structured into three chapters: Chapter 1 (Introduction) provides background, objectives, and methodology; Chapter 2 (Face Recognition System) details the system's implementation and challenges; and Chapter 3 (Literature Survey) reviews existing research on face recognition, including comparisons of dimension reduction techniques.

What are the key findings or contributions of this research (as previewed)?

The preview highlights the implementation of a real-time face recognition system using LBP, an analysis of its efficiency and challenges, and a comparison of relevant dimension reduction techniques. Specific quantitative results and contributions are not detailed in this preview.

What are the keywords associated with this research?

Face recognition, real-time processing, Local Binary Patterns (LBP), feature extraction, face detection, dimension reduction, OpenCV, Olivetti database, FERET database, biometrics, image processing, computer vision.