DEVELOPMENT OF A PORTABLE FINGERPRINT ATTENDANCE SYSTEM FOR UNIVERSITY STUDENT

As a partial fulfillment of Final Year Project, the main aim of this project is to develop an accurate, fast and very efficient portable attendance system using fingerprint verification technique. This portable fingerprint attendance management system is specially designed and implemented for university student. This system is based on biometrics, software and database technique to solve the problem of spurious attendance and any else made by the students and others. The current product of fingerprint attendance systems offers minimal, nonflexible and costly system. With an increasing awareness of efficiency of fingerprint attendance system, the implementation of portable fingerprint attendance system has gain interest. The implementation is achieved using a combination of hardware and software development. A Motorola handphone model Atrix 4G is being used as a hardware device while software development is based on the Android operating system. Software called Eclipse is used for the development of android application which based on the Java script language. In this paper, the result of the system were shown sequentially, demonstrating that the system able to run safely and efficient in any real-time conditions

Fingerprint image enhancement using fully convolutional deep autoencoders / Destaque de imagens de impressão digital utilizando autoencoders profundos totalmente convolucionais

Image quality for fingerprint samples is critical for the matching process. Novel methods introduce deep learning matching techniques based on convolutions neural networks to enhance degraded fingerprint images. However, due to the nature of the enhanced image problem, these methods tend to rely on processing small image patches to achieve their goal. Such an approach may often yield satisfactory results while having high computational costs due to overlapping in patches. In this paper, we propose a fast and accurate fully convolutional neural network based on an auto-encoder architecture to enhance the quality of fingerprint images. We do not use the patch processing method and instead train a model to enhance the image as a whole. After exhaustive testing, we achieve a model that can quickly perform the desired task, while achieving an average of 97.956% and 83.748% per pixel accuracy on the easiest and hardest dataset respectively. The models were trained on the publicly available Fingerprint Verification Competition datasets. We then highlight the most general model that can best enhance the quality of all datasets

Likelihood-Ratio-Based Biometric Verification

The paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that, for single-user verification, the likelihood ratio is optimal. Second, we show that, under some general conditions, decisions based on posterior probabilities and likelihood ratios are equivalent and result in the same receiver operating curve. However, in a multi-user situation, these two methods lead to different average error rates. As a third result, we prove theoretically that, for multi-user verification, the use of the likelihood ratio is optimal in terms of average error rates. The superiority of this method is illustrated by experiments in fingerprint verification. It is shown that error rates below 10/sup -3/ can be achieved when using multiple fingerprints for template construction

A first step to accelerating fingerprint matching based on deformable minutiae clustering

Fingerprint recognition is one of the most used biometric methods for authentication. The identification of a query fingerprint requires matching its minutiae against every minutiae of all the fingerprints of the database. The state-of-the-art matching algorithms are costly, from a computational point of view, and inefficient on large datasets. In this work, we include faster methods to accelerating DMC (the most accurate fingerprint matching algorithm based only on minutiae). In particular, we translate into C++ the functions of the algorithm which represent the most costly tasks of the code; we create a library with the new code and we link the library to the original C# code using a CLR Class Library project by means of a C++/CLI Wrapper. Our solution re-implements critical functions, e.g., the bit population count including a fast C++ PopCount library and the use of the squared Euclidean distance for calculating the minutiae neighborhood. The experimental results show a significant reduction of the execution time in the optimized functions of the matching algorithm. Finally, a novel approach to improve the matching algorithm, considering cache memory blocking and parallel data processing, is presented as future work.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech