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
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
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
3.3 Literature Gap
3.4 Objective of Present Study
4 DIMENSION REDUCTION TECHNIQUES
4.1 Local Binary Pattern (LBP)
4.1.1 Overview
4.1.2 How LBP Works?
4.1.3 Properties of LBP
4.2 LBP Operator
4.3 Flow Chart of LBP Process
4.4 Face description using LBP
4.5 LBP Applications
5 RESULTS AND DISCUSSIONS
5.1 LBP Circular Histogram
5.1.1 Flow Chart of LBP Circular Histogram Process
5.2 Database Creation
5.3 LBP Frames
5.3.2 LBP 8-bit frame
Features:
5.4 Optimised System
5.5 Maximum Likelihood Prediction
5.6 Results
5.7 Overcome of My Problem Statement
5.8 Limitation
6 CONCLUSION & FUTURE SCOPE
6.1 Conclusion
6.2 Future Scope
Research Objectives and Focus Areas
The primary objective of this thesis is to develop an efficient, real-time face recognition system using the Local Binary Pattern (LBP) algorithm with OpenCV to achieve both low recognition time and high accuracy.
- Enhancement of the frame-per-second (fps) rate to ensure real-time performance.
- Comprehensive study and comparison of different LBP frame bit-rates (8-bit, 10-bit, 16-bit).
- Implementation of a custom face database to optimize performance in high-resolution settings.
- Introduction of a "Maximum Likelihood Prediction" loop to minimize false negatives and boost overall system confidence.
- Optimization of LBP-based feature extraction for robust face representation.
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1.1 Background
Biometrics research investigates methods and techniques for recognizing humans based on their behavioural and physical characteristics or traits (Jain, Ross, & Prabhakar, 2004; Mohamed et al., 2011; Mohamed et al., 2012; Mohamed & Yampolskiy, 2012d; Wayman, 2001; Zhenhua, Lei, Zhang, & Xuanqin, 2010) [39,35]. Face recognition is a biometric trait and it is something that people usually perform effortlessly and routinely in their everyday life and it is the process of identifying individuals from their faces’ intrinsic characteristics. Automated face recognition has become one of the main targets of investigation for researchers in biometrics, pattern recognition, computer vision, and machine learning communities. This interest is driven by a wide range of commercial and law enforcement practical applications that require the use of face recognition technologies (Mohamed et al., 2012; Mohamed & Yampolskiy, 2012d) [35]. These applications include access control, automated crowd surveillance, face reconstruction, mugshot identification, human-computer interaction and multimedia communication (Haiping, Martin, Bui, Plataniotis, & Hatzinakos, 2009; Mohamed et al., 2012; Mohamed & Yampolskiy, 2012d; Phillips, Martin, Wilson, & Przybocki, 2000; Wayman, 2001) [31, 35].
Face recognition systems have many advantages over traditional security systems: the biometric identification of a person cannot be lost, forgotten like complex passwords and PIN codes or easy to be guessed by an illegitimate user like short and simple passwords (Chan, 2008; Li & Jain, 2011) [32].
Summary of Chapters
CHAPTER 1: INTRODUCTION: This chapter provides the research motivation, problem statement, and objectives, while introducing the software used and the scope of the thesis.
2 FACE RECOGNITION SYSTEM: An overview of face recognition concepts, applications in real-time, and a classification of face detection and identification methodologies.
3 LITERATURE SURVEY: A detailed review of existing research in face recognition, including various dimension reduction techniques and their performance.
4 DIMENSION REDUCTION TECHNIQUES: Explains the theoretical foundation of the Local Binary Pattern (LBP) operator, its properties, and how it is applied to face description.
5 RESULTS AND DISCUSSIONS: Presents the implementation of the proposed system, including database creation, evaluation of different LBP frame sizes, and the introduction of the Maximum Likelihood Prediction method.
6 CONCLUSION & FUTURE SCOPE: Summarizes the research findings and discusses potential future improvements for the LBP-based real-time face recognition system.
Keywords
Face Recognition, Local Binary Pattern, LBP, Real-Time Systems, Biometrics, Image Processing, OpenCV, Dimension Reduction, Feature Extraction, Face Detection, Maximum Likelihood Prediction, Pattern Recognition, Texture Analysis, Surveillance, Database Registration.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on creating an efficient face recognition system for real-time applications that balances high accuracy with low processing time using the Local Binary Pattern algorithm.
What are the primary thematic areas covered?
The thesis covers face recognition methodologies, feature extraction using LBP, real-time image processing, dimension reduction techniques, and performance benchmarking for different LBP frame configurations.
What is the main objective or research question?
The main objective is to reduce the computational time required for face recognition to enable real-time operation while maintaining high accuracy in high-resolution video streams.
Which scientific methodology is primarily used?
The study utilizes the Local Binary Pattern (LBP) operator as the primary texture descriptor and dimension reduction technique, implemented via OpenCV on a Linux platform.
What does the main body of the work address?
The main body covers the theoretical background of face recognition, a comprehensive survey of literature, detailed descriptions of LBP variations (16-bit, 8-bit, 10-bit), and the development of a custom face database.
Which keywords best characterize this work?
The key terms include Face Recognition, Local Binary Pattern (LBP), Real-Time Processing, Biometrics, and Maximum Likelihood Prediction.
How does the "Maximum Likelihood Prediction" loop function?
It is a post-processing step that analyzes three consecutive predictions; it provides a high-confidence result only if all three match, significantly reducing false negatives in security-sensitive scenarios.
Why are different LBP frame bit-rates compared?
The study compares them to determine the optimal trade-off between speed and accuracy, finding that different bit-rates influence confidence levels and processing time differently.
What were the major limitations identified in the study?
The system's performance currently degrades under varying or non-optimal lighting conditions, which the author suggests can be addressed through better hardware light sources in future iterations.
- Citar trabajo
- Pradeep Kakkar (Autor), 2017, Face Recognition for Real Time Application, Múnich, GRIN Verlag, https://www.grin.com/document/380686
