"Automated Face Recognition System using CLBP in MATLAB"

Problem Definition

Problem Description: The current face recognition systems face issues with complexity and accuracy, leading to inefficient and unreliable results. These systems require high computational resources and often struggle to accurately identify individuals in varying lighting conditions or angles. There is a need for a more efficient face recognition methodology that can reduce complexity and improve accuracy in biometric authentication applications. This can be achieved by utilizing the CLBP technique for feature extraction and matching of facial images to ensure a more reliable and secure system. By implementing a face recognition system based on the CLBP technique in MATLAB software, the complexity can be reduced, allowing for quicker and more accurate identification of individuals in various scenarios.

This will result in a more reliable and secure biometric authentication system that can be effectively used for surveillance, database indexing, and identity verification purposes.

Proposed Work

The proposed work focuses on the development of a face recognition system using the Circular Local Binary Pattern (CLBP) technique implemented in MATLAB software. Face recognition systems are essential for biometric authentication and surveillance applications, as they provide non-intrusive and efficient identification of individuals from digital images or video frames. In this project, image datasets are converted into linear binary patterns (LBP) to extract powerful texture features for matching images. The CLBP technique is utilized to improve the complexity of face recognition systems. The system involves the selection and extraction of features from images in the dataset, followed by matching with new image datasets to identify individuals.

The system's authentication process is based on matching features extracted using the LBP technique. This automated security system ensures accurate and reliable biometric identification, enhancing overall security measures. This project falls under the Image Processing & Computer Vision category, specifically focusing on Face Recognition based Systems within the Security, Authentication & Identification Systems subcategory. The implementation of regulated power supply, IR transceiver as a proximity sensor, and MATLAB GUI modules contributes to the successful development of this innovative face recognition methodology.

Application Area for Industry

This face recognition system based on the CLBP technique can be applied in various industrial sectors such as security, banking, healthcare, and retail. In the security sector, this project can be used for surveillance purposes, ensuring accurate identification of individuals for access control or monitoring. In the banking sector, this system can enhance security measures for identity verification during transactions or access to secure areas. For healthcare, this project can be implemented for patient identification and access to medical records, improving efficiency and accuracy in healthcare settings. In the retail sector, this system can be utilized for customer identification and personalized services, enhancing the overall shopping experience.

The proposed solutions of utilizing the CLBP technique for feature extraction and matching in the face recognition system address specific challenges faced by industries, such as complexity, accuracy, and efficiency. By reducing the complexity of the system and improving accuracy in identifying individuals in varying conditions, this project offers a more reliable and secure biometric authentication system for different industrial domains. The benefits of implementing these solutions include quicker and more accurate identification of individuals, enhanced security measures, and improved efficiency in access control and authentication processes. Overall, this project's innovative approach to face recognition systems can contribute to the advancement of security, authentication, and identification systems across various industries.

Application Area for Academics

The proposed project on developing a face recognition system using the Circular Local Binary Pattern (CLBP) technique in MATLAB software holds significant relevance for MTech and PHD students in the field of Image Processing & Computer Vision. This project addresses the current challenges faced by face recognition systems in terms of complexity and accuracy, offering a solution that can improve the efficiency and reliability of biometric authentication applications. MTech and PHD students can use this project for innovative research methods, simulations, and data analysis for their dissertations, theses, or research papers. They can explore different applications of the CLBP technique for feature extraction and matching of facial images to enhance the accuracy of identification in various scenarios. By utilizing the code and literature of this project, researchers can delve into advanced research methods in the domain of security, authentication, and identification systems, specifically focusing on Face Recognition based Systems.

The project can serve as a foundation for developing advanced face recognition methodologies and can be further extended to incorporate other biometric authentication techniques for a more comprehensive security system. The future scope of this project includes integrating machine learning algorithms for facial recognition to enhance the system's accuracy and performance, providing ample opportunities for MTech students and PHD scholars to contribute to cutting-edge research in this field.

Keywords

face recognition, CLBP technique, MATLAB software, biometric authentication, surveillance, database indexing, identity verification, image datasets, texture features, linear binary patterns, security, Image Processing, Computer Vision, Security Systems, Authentication Systems, Image Recognition, biometrics, PCA, Neural Network, SVM, Eigen, Classifier, Access Control Systems, Authentication, Identification, Computer Vision, Image Acquisition, Recognition, Matching, Face Expression Recognition, Gesture Recognition, Neurofuzzy, Ann, Histogram