Hybrid Feature Extraction with Grey Wolf Optimization for Finger Vein Recognition

Problem Definition

The field of finger vein recognition faces multiple challenges that hinder the achievement of a satisfactory level of classification performance. Vein thickness, inconsistencies in illumination, low contrast sections, image deformation, and existing noise all contribute to the difficulty in accurately extracting features from finger vein images. Additionally, the scattering of light and finger translation can result in blurred images, further complicating the recognition process. The high dimensionality of features leads to substantial computation and memory costs during classifier training and classification, which in turn affects the accuracy of feature extraction and degrades the overall recognition performance of the system. Previous attempts at feature extraction using Local Binary Pattern (LBP) have shown limited success in handling arbitrary noise and blur, reinforcing the need for a more robust technique such as LPQ.

By proposing LPQ as a more descriptive and discriminative feature extraction method that is invariant to optical image blur and uniform illumination changes, this paper aims to address the existing limitations and improve the efficiency and accuracy of finger vein recognition systems.

Objective

The objective of this research is to improve the efficiency and accuracy of finger vein recognition systems by addressing the existing limitations and challenges. This will be achieved by proposing Local Phase Quantization (LPQ) as a more robust feature extraction technique that is invariant to optical blur and uniform illumination changes. By combining LPQ with Local Directional Pattern (LDP) and using the Grey Wolf Optimization (GWO) algorithm for SVM, the aim is to enhance classification accuracy and overcome issues related to vein thickness, illumination inconsistencies, image deformation, noise, and blur in finger vein images. The ultimate goal is to develop a more reliable biometric security solution through the utilization of advanced algorithms and techniques.

Proposed Work

The proposed work aims to address the limitations and challenges faced in finger vein recognition by introducing a robust feature extraction technique called Local Phase Quantization (LPQ). The research has identified the shortcomings in existing methods such as low classification performance due to factors like vein thickness, illumination inconsistencies, and image deformation. By combining LPQ with Local Directional Pattern (LDP) and utilizing the Grey Wolf Optimization (GWO) algorithm for SVM, the objective is to achieve higher classification accuracy. The rationale behind these choices is that LPQ offers descriptive and discriminative features that are invariant to optical blur and illumination changes, while GWO-SVM maximizes the classification accuracy by optimizing the parameters. Furthermore, the proposed framework involves pre-processing steps to extract a robust region of interest (ROI) from finger vein images, followed by hybrid feature extraction using LPQ and LDP.

This combination aims to overcome challenges related to noise, blur, and misalignment in the images, ultimately improving recognition performance. By utilizing advanced algorithms and techniques, the project seeks to enhance the efficiency and accuracy of finger vein recognition systems, contributing towards the development of more reliable biometric security solutions.

Application Area for Industry

This project can be utilized in various industrial sectors such as banking, healthcare, security, and access control systems. In the banking sector, the implementation of the proposed finger vein recognition system can enhance the security of customer transactions by providing a more accurate and reliable biometric authentication method. In healthcare, the accurate identification of patients can help in preventing medical identity theft and ensuring the privacy of personal health information. Security and access control systems can benefit from the robust feature extraction technique to improve the efficiency and accuracy of identifying authorized individuals. The challenges faced by these industries, such as the need for secure and reliable identification methods, can be addressed by implementing the proposed solutions.

The benefits of using the hybrid feature extraction technique combining LPQ and LDP, along with Grey Wolf Optimization based SVM, include improved accuracy in finger vein recognition, robustness to noise and blur, and efficient computation. Overall, the project's solutions can help in enhancing security measures, improving authentication processes, and ensuring the privacy and confidentiality of sensitive information across various industrial domains.

Application Area for Academics

The proposed project on finger vein recognition using a hybrid feature extraction technique has the potential to enrich academic research, education, and training in the field of biometrics and image processing. This project addresses the limitations of existing finger vein recognition systems by proposing a robust feature extraction technique that combines LPQ and LDP, along with GWO-SVM classification for improved accuracy. Researchers in the field of biometrics and image processing can benefit from this project by exploring innovative research methods in feature extraction and classification algorithms. The proposed framework can serve as a valuable tool for conducting simulation studies and data analysis in educational settings, helping students and scholars gain practical insights into the complexities of finger vein recognition systems. The code and literature of this project can be used by field-specific researchers, MTech students, and PhD scholars to further their research in biometrics, image processing, and machine learning.

By implementing the proposed hybrid feature extraction technique, researchers can enhance the performance of existing finger vein recognition systems and explore new avenues for improvement. In the future, this project opens up possibilities for exploring additional technologies such as deep learning algorithms and extending the framework to other biometric modalities. The robust feature extraction technique proposed in this project lays the foundation for future research in the field of biometrics, offering new opportunities for innovation and advancement in the domain of finger vein recognition.

Algorithms Used

LPQ is used to extract local texture information from finger vein images, whereas LDP is employed to capture directional patterns within the images. This hybrid feature extraction technique ensures that the extracted features are robust and invariant to various common image distortions. Grey Wolf Optimization is then applied to optimize the SVM classifier's parameters for improving classification accuracy. By combining these algorithms, the proposed framework aims to enhance the accuracy of finger vein recognition while also improving efficiency in the classification process.

Keywords

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SEO Tags

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