Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. The rapidly expanding research in face processing is based on the premise that information about a user’s identity, state, and intent can be extracted from images and that computers can then react accordingly, e.g., by knowing person’s identity, person may be authenticated to utilize a particular service or not. A first step of any face processing system is registering the locations in images where faces are present. The local binary pattern is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The LBP method can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its invariance against monotonic gray level changes caused, e.g., by illumination variations. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. The success of LBP in face description is due to the discriminative power and computational simplicity of the LBP operator, and the robustness of LBP to mono-tonic gray scale changes caused by, for example, illumination variations. The use of histograms as features also makes the LBP approach robust to face misalignment and pose variations. For these reasons, the LBP methodology has already attained an established position in face analysis research. Because finding an efficient spatiotemporal representation for face analysis from videos is challenging, most of the existing works limit the scope of the problem by discarding the facial dynamics and only considering the structure. Motivated by the psychophysical findings which indicate that facial movements can provide valuable information to face analysis, spatiotemporal LBP approaches for face, facial expression and gender recognition from videos were described.
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.
- Citar trabajo
- Pradeep Kakkar (Autor), 2017, Face Recognition for Real Time Application, Múnich, GRIN Verlag, https://www.grin.com/document/380686
