Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Document Type
Event
Start Date
25-4-2024 4:00 PM
Description
In recent years, the field of big data analytics has gained immense attention due to the increasing volume and complexity of data being generated from various sources. One of the key applications of big data analytics is in the field of identity verification, where it is used to process and analyze large amounts of biometric data to authenticate individuals. A hybrid biometric system that combines multiple biometric modalities has been shown to be more effective in identity verification than a single modality system. In this project, we propose an Identity Verification Framework using a Hybrid Biometric System that leverages big data analytics techniques to improve the accuracy and efficiency of the verification process. The framework uses a combination of iris recognition and face recognition modalities to authenticate individuals. The proposed framework involves several stages, including data collection, preprocessing, feature extraction, and classification. The dataset used in the project includes 460 images in total, including 5 photographs of each left and right iris from 46 individuals. Iris segmentation is used to extract the iris region, and Gabor wavelets are used to extract texture features from the iris image. Face detection and recognition are performed using OpenCV's Haar Cascade classifier and deep learning-based face recognition models, respectively.
Included in
GMR-7 A Novel Identity Verification Framework using a Hybrid Biometric System
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
In recent years, the field of big data analytics has gained immense attention due to the increasing volume and complexity of data being generated from various sources. One of the key applications of big data analytics is in the field of identity verification, where it is used to process and analyze large amounts of biometric data to authenticate individuals. A hybrid biometric system that combines multiple biometric modalities has been shown to be more effective in identity verification than a single modality system. In this project, we propose an Identity Verification Framework using a Hybrid Biometric System that leverages big data analytics techniques to improve the accuracy and efficiency of the verification process. The framework uses a combination of iris recognition and face recognition modalities to authenticate individuals. The proposed framework involves several stages, including data collection, preprocessing, feature extraction, and classification. The dataset used in the project includes 460 images in total, including 5 photographs of each left and right iris from 46 individuals. Iris segmentation is used to extract the iris region, and Gabor wavelets are used to extract texture features from the iris image. Face detection and recognition are performed using OpenCV's Haar Cascade classifier and deep learning-based face recognition models, respectively.