Comprehensive Person Profiling System-Reference-Cited by-同舟云学术

Comprehensive Person Profiling System

Published:2024-05-15 Issue: Volume: Page:420-425
ISSN:2581-9429
Container-title:International Journal of Advanced Research in Science, Communication and Technology
language:en
Short-container-title:IJARSCT

Author:

Chikondi Lizzie Mkomba ¹,Fanny Chatola ¹

Affiliation:

1. DMI St Joseph the Baptist University, Lilongwe, Malawi

Abstract

The proposed Comprehensive person profiling system aims to use innovative approach to revolutionizing identity verification in financial transactions, particularly within banks or other financial organizations. This project aims to replace traditional national ID usage with a more efficient and secure system. Leveraging advanced technologies such as biometrics, data analytics, and machine learning, the system ensures a comprehensive profiling of individuals, offering a robust alternative to conventional identification methods. By seamlessly integrating into financial institutions, this solution enhances inconveniences of the users’ doing transactions, security, minimizes identity fraud, and streamlines customer authentication processes. The Comprehensive Person Profiling System not only safeguards sensitive information but also enhances the overall efficiency and reliability of financial transactions, marking a significant leap towards a more secure and technologically advanced future in the realm of personal identification.

Publisher

Naksh Solutions

Reference10 articles.

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2. Abiantun, R. et al. in 2020 “Face Net: A unified Embedding for Face Recognition and clustering”- Proposes FaceNet, which achieves state-of-the-art results in face recognition and clustering.

3. Wang, X.& Ji, Q. in 2018 A Survey of 3D Face Recognition Methods”- Provides an overview of 3D face recognition techniques and their applications for person profiling

4. Parkhi, O.M. et al. in 2015 “Deep Face Recognition”-Discusses deep learning methods for face recognition, with a focus on convolutional neural networks and their impact on person profiling.

5. Bhagavatula, C. et al. in 2017 FACENET2SVM: A Facial Expression Recognition System”- Presents a system that combines facial expression recognition with identity recognition for comprehensive profiling.