Fingerprint recognition system

Undergraduate thesis submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Management Information Systems, May 2022Fingerprint recognition is one of the most popular biometric techniques in personal identification. The widespread use of fingerprint recognition as a biometric is because each fingerprints pattern of ridges and valleys is unique and does not vary with time and age. While there are several algorithms or methods for fingerprint recognition systems, the quest to develop a robust fingerprint recognition system remains a significant research area. One major challenge in designing a system for fingerprint recognition is its ability to perform well on both full and partial fingerprint images. Most fingerprint recognition systems developed so far use minutiae-based algorithms which tend to perform well under full fingerprint but poorly under partial occlusion. In partial occlusion, the minutiae, which are the core points of the fingerprints, get completely distorted. The distortion of minutiae makes minutiae-based algorithms perform poorly. Therefore, this study proposed a novel non-minutiae-based algorithm that adapts the Fisherface and Eigenface method from facial recognition. The proposed algorithm is insensitive to partial occlusion. The Eigenface and Fisherface methods were tested on FVC 2002 Datasets and yielded an accuracy of 86.67% and 90% respectively. These accuracy results indicates that there is a possibility of recognizing fingerprint images using non-minutiae-based algorithms from different domain.Ashesi Universit

Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture

Fingerprint recognition systems have been applied widely to adopt accurate and reliable biometric identification between individuals. Deep learning, especially Convolutional Neural Network (CNN) has made a tremendous success in the field of computer vision for pattern recognition. Several approaches have been applied to reconstruct fingerprint images. However, these algorithms encountered problems with various overlapping patterns and poor quality on the images. In this work, a convolutional neural network autoencoder has been used to reconstruct fingerprint images. An autoencoder is a technique, which is able to replicate data in the images. The advantage of convolutional neural networks makes it suitable for feature extraction. Four datasets of fingerprint images have been used to prove the robustness of the proposed architecture. The dataset of fingerprint images has been collected from various real resources. These datasets include a fingerprint verification competition (FVC2004) database, which has been distorted. The proposed approach has been assessed by calculating the cumulative match characteristics (CMC) between the reconstructed and the original features. We obtained promising results of identification rate from four datasets of fingerprints images (Dataset I, Dataset II, Dataset III, Dataset IV) with 98.1%, 97%, 95.9%, and 95.02% respectively by CNN autoencoder. The proposed architecture was tested and compared to the other state-of-the-art methods. The achieved experimental results show that the proposed solution is suitable for recreating a complex context of fingerprinting images

Effective Data Analytics and Security Strategies in Internal Audit Organizations

The digitization of the corporate and regulatory environment presents an opportunity for internal audit organizations to change their audit techniques and increase their value to corporations. Audit functions have not kept pace with these advancements, as evidenced by the massive frauds in recent years, and current audit methodology does not robustly incorporate analytics and security of data. Grounded in agency theory, the purpose of this qualitative case study was to explore successful strategies business leaders use to implement data analytics and security for internal auditing and fraudulent activity. The participants comprised 3 audit leaders in Pennsylvania, who effectively used data analytics and security strategies to promote quality audits and detect fraud. Data collected from semistructured interviews and company documents facilitated thematic analysis. Four themes emerged: data analytics framework, human capital, technology, and stakeholder engagement. A key recommendation for successful implementation of data analytics is a strategy to staff the audit function with the appropriate level of IT and finance skills, which would reduce the overall cost of implementation. The implications for positive social change include the potential to increase confidence in financial statements and the potential for job opportunities and support of economic growth in the local communities