UTP Cafeteria Cashless Payment System using Fingerprint Recognition

Cashless payment system is one of the most significant improvements in food service and is transforming today's quick service especially for campus or high school cafeteria. There are several medium for cashless payment system which include using smart cards or credit cards and biometric technologies. There are many problem associated with smart cards and credit cards technologies since people might forget the password. With cards to identify a person, whoever has possession of the card can again access. Biometric technologies have its different features to remedy this problem. A biometric system cannot be implemented the same way as one utilizing password since there will be no physical items. One reason a biometric system is valuable is in the way it authenticate users. A biometric system offers levels of physical security, integrity and confidentiality. This research focuses on the development of Cashless Payment System for UTP Cafeteria using fingerprint recognition, a system which enables students to make payment for foods that they purchased using the touch of their finger. Intensive research and literature review had been done in order to obtain as much information to develop the project. Surveys had been done over some group of students to obtain data on their feedback regarding the development of the system. The project will solve problems that arise from the cash based system which is currently used by all cafe owners in UTP

Documentation and analysis of plastic fingerprint impressions involving contactless three-dimensional surface scanning

Fingerprint impressions are frequently encountered during the investigation of crime scenes, and may establish a crucial linkage between the suspect and the crime scene. Plastic fingerprint impressions found at crime scenes are often transient and delicate, leaving photography the sole means of documentation. A traditional photography approach can be inadequate in documenting impressions that contain three-dimensional (3D) details due to the limitations of camera and lighting conditions on scene. In this study, 3D scanning was proposed as a novel method for the documentation of plastic fingerprints. Structured-light 3D scanning (SLS) captures the distortion of projected light patterns on the subject to obtain its 3D profile, which allows fast acquisition of the complete 3D geometric information of the surface. The contactless operation of SLS also eliminates the risk of destroying fragile evidence, making it a sound choice for forensic applications. In this study, the feasibility of 3D scanning of plastic fingerprint impressions was evaluated and compared with traditional photography regarding the quantity and quality of perceptible friction ridge features. Attempts were made to develop a procedure to extract curvature features from 3D scanned fingerprints and flatten the friction ridge features into two-dimensional (2D) images to allow direct comparison with the traditional photography method in the CSIpix® Matcher and NFIQ 2.0 software. One of the developed methods (3DR) utilizing a discrete geometry operator and convexity features outperformed traditional photography, both in minutiae count and match quality, while traditional photography could not always capture enough high-quality minutiae for comparisons, even after digital enhancement. The reproducibility of the 3D scanning process was evaluated using 3D point cloud statistics. The pair-wise mean distance and standard deviation were calculated for four levels of comparisons with theoretically increasing disparity, including pairs of scans of the same impressions. The results showed minimal shape deviation from scan to scan for the same impression, but high variations for different impressions

A Biometric Approach to Prevent False Use of IDs

What is your username? What is your password? What is your PIN number? These are some of the commonly used key questions users need to answer accurately in order to verify their identity and gain access to systems and their own data. Passwords, Personal Identification Numbers (PINs) and ID cards are different means of tokens used to identify a person, but these can be forgotten, stolen or lost. Currently, University of Hertfordshire (UH) carries out identity checks by checking the photograph on an ID card during exams. Other processes such as attendance monitoring and door access control require tapping the ID card on a reader. These methods can cause issues such as unauthorised use of ID card on attendance system and door access system if ID card is found, lost or borrowed. During exams, this could lead to interruptions when carrying out manual checks. As the invigilator carries out checks whilst the student is writing an exam, it is often difficult to see the student’s face as they face down whilst writing the exam. They cannot be disturbed for the ID check process. Students are also required to sign a manual register as they walk into the exam room. This process is time consuming. A more robust approach to identification of individuals that can avoid the above mentioned limitations of the traditional means, is the use of biometrics. Fingerprint was the first biometric modality that has been used. In comparison to other biometric modalities such as signature and face recognition, fingerprint is highly unique, accepted and leads to a more accurate matching result. Considering these properties of fingerprint biometrics, it has been explored in the research study presented in this thesis to enhance the efficiency and the reliability of the University’s exam process. This thesis focuses on using fingerprint recognition technology in a novel approach to check identity for exams in a University environment. Identifying a user using fingerprints is not the only aim of this project. Convenience and user experience play vital roles in this project whilst improving speed and processes at UH

Analysis Of Data Stratification In A Multi-Sensor Fingerprint Dataset Using Match Score Statistics

Biometric data is an essential feature employed in testing the performance of any real time biometric recognition system prior to its usage. The variations introduced in the match performance critically determine the authenticity of the biometric data to be able to be used in an everyday scenario for the testing of biometric verification systems. This study in totality aims at understanding the impact of data stratification of a such a biometric test dataset on the match performance of each of its stratum. In order to achieve this goal, the fingerprint dataset of the West Virginia University\u27s 2012 BioCOP has been employed which is a part of the many multimodal biometric data collection projects that the University has accomplished. This test dataset has been initially segmented based on the scanners employed in the process of data acquisition to check for the variations in match performance with reference to the acquisition device. The secondary stage of data stratification included the creation of stratum based on the demographic features of the subjects in the dataset.;The main objectives this study aims to achieve are:;• Developing a framework to assess the match score distributions of each stratum..;• Assessing the match performance of demographic strata in comparison to the total dataset..;• Statistical match performance evaluation using match score statistics..;Following the generation of genuine and imposter match score distributions , Receiver Operating Characteristic Curves (ROC) were plotted to compare the match performance of each demographic stratum with respect to the total dataset. The divergence measures KLD and JSD have been calculated which signify the amount of variation between the match score distributions of each stratum. With the help of these procedures, the task of estimating the effect of data stratification on the match performance has been accomplished which serves as a measure of understanding the impact of this fingerprint dataset when used for biometric testing purposes

Indexing techniques for fingerprint and iris databases

This thesis addresses the problem of biometric indexing in the context of fingerprint and iris databases. In large scale authentication system, the goal is to determine the identity of a subject from a large set of identities. Indexing is a technique to reduce the number of candidate identities to be considered by the identification algorithm. The fingerprint indexing technique (for closed set identification) proposed in this thesis is based on a combination of minutiae and ridge features. Experiments conducted on the FVC2002 and FVC2004 databases indicate that the inclusion of ridge features aids in enhancing indexing performance. The thesis also proposes three techniques for iris indexing (for closed set identification). The first technique is based on iriscodes. The second technique utilizes local binary patterns in the iris texture. The third technique analyzes the iris texture based on a pixel-level difference histogram. The ability to perform indexing at the texture level avoids the computational complexity involved in encoding and is, therefore, more attractive for iris indexing. Experiments on the CASIA 3.0 database suggest the potential of these schemes to index large-scale iris databases

A new algorithm for minutiae extraction and matching in fingerprint

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A novel algorithm for fingerprint template formation and matching in automatic fingerprint recognition has been developed. At present, fingerprint is being considered as the dominant biometric trait among all other biometrics due to its wide range of applications in security and access control. Most of the commercially established systems use singularity point (SP) or ‘core’ point for fingerprint indexing and template formation. The efficiency of these systems heavily relies on the detection of the core and the quality of the image itself. The number of multiple SPs or absence of ‘core’ on the image can cause some anomalies in the formation of the template and may result in high False Acceptance Rate (FAR) or False Rejection Rate (FRR). Also the loss of actual minutiae or appearance of new or spurious minutiae in the scanned image can contribute to the error in the matching process. A more sophisticated algorithm is therefore necessary in the formation and matching of templates in order to achieve low FAR and FRR and to make the identification more accurate. The novel algorithm presented here does not rely on any ‘core’ or SP thus makes the structure invariant with respect to global rotation and translation. Moreover, it does not need orientation of the minutiae points on which most of the established algorithm are based. The matching methodology is based on the local features of each minutiae point such as distances to its nearest neighbours and their internal angle. Using a publicly available fingerprint database, the algorithm has been evaluated and compared with other benchmark algorithms. It has been found that the algorithm has performed better compared to others and has been able to achieve an error equal rate of 3.5%

Facilitating sensor interoperability and incorporating quality in fingerprint matching systems

This thesis addresses the issues of sensor interoperability and quality in the context of fingerprints and makes a three-fold contribution. The first contribution is a method to facilitate fingerprint sensor interoperability that involves the comparison of fingerprint images originating from multiple sensors. The proposed technique models the relationship between images acquired by two different sensors using a Thin Plate Spline (TPS) function. Such a calibration model is observed to enhance the inter-sensor matching performance on the MSU dataset containing images from optical and capacitive sensors. Experiments indicate that the proposed calibration scheme improves the inter-sensor Genuine Accept Rate (GAR) by 35% to 40% at a False Accept Rate (FAR) of 0.01%. The second contribution is a technique to incorporate the local image quality information in the fingerprint matching process. Experiments on the FVC 2002 and 2004 databases suggest the potential of this scheme to improve the matching performance of a generic fingerprint recognition system. The final contribution of this thesis is a method for classifying fingerprint images into 3 categories: good, dry and smudged. Such a categorization would assist in invoking different image processing or matching schemes based on the nature of the input fingerprint image. A classification rate of 97.45% is obtained on a subset of the FVC 2004 DB1 database

An Approach to Identify Imprints using Image Processing

Volume 1 Issue 4 (June 2013