Fingerprint Pore Detection: A Survey

This work presents the first survey on fingerprint pore detection. The survey provides a general overview of the field and discusses methods, datasets, and evaluation protocols. We also present a baseline method inspired on the state-of-the-art that implements a customizable Fully Convolutional Network, whose hyperparameters were tuned to achieve optimal pore detection rates. Finally, we also reimplementated three other approaches proposed in the literature for evaluation purposes. We have made the source code of (1) the baseline method, (2) the reimplemented approaches, and (3) the training and evaluation processes for two different datasets available to the public to attract more researchers to the field and to facilitate future comparisons under the same conditions. The code is available in the following repository: https://github.com/azimIbragimov/Fingerprint-Pore-Detection-A-Surve

Система обробки відбитків пальців

Досліджено особливості обробки зображень відбитків пальців (дактилоскопічних зображень). Наведено критерії вибору діапазонів дискретизації та квантування зображень. Вирішені питання зменшення розміру зображення відбитку пальця при збільшенні контрастності зображення. Зменшення обсягу зображення базується на використанні інтерполяції. Показано, що серед розглянутих методів інтерполяції - лінійної, білінійної та бікубічної - остання має найбільшу точність. Однак, при значенні роздільної здатності (dpi) менше 150 спостерігається наявність значної кількості артефактів на зображенні. Підвищення різкості можливо досягти в результаті застосування оператора Лапласа (обчислення Лапсасіану) та додавання результату до початкового зображення. Виконання цієї операції дозволяє отримати прийнятний баланс між швидкодією та обчислювальною складністю алгоритму розпізнавання відбитків пальців. Наведена технічна реалізація пристрою та ілюстрація його роботи.Recognition of fingerprints (dactyloscopic images) is one of the practical application of signal processing. Sys-tem of person identification by fingerprints is commonly-used by law enforcement bodies and Border services. This is also important in the field of access control systems and commercial devices where data security is not less important as reliability and data rate of processing algorithms. Existing systems of fingerprints processing are not fully ready for automatic recog-nition. Also, full modernization of existing equipment is not possible. The paper is devoted to the method of image processing. In particular, the preliminary processing of dactyloscopic images is considered as well as development of theoretical approach and practical realization of first stage of patterns forming — pre-processing of image for decreasing of its size and contrast increasing. The criteria for selecting ranges for sampling and quantization of images are given. Tasks of reducing the fingerprint image while increasing the contrast of the image were considered, analyzed and solved. Image reduction is based on the use of interpolation. It is shown that among the considered interpolation methods — linear, bilinear and bicubic - the latter one could provide the highest accuracy although it needs more hardware resources. However, when the dpi parameter (dots-per-inch) falls below 150, a rapid increase in the number of artifacts in the image is observed. Increasing of image sharpness is necessary for highlighting of colour transitions and consequently — for increasing the percentage of correct recognitions. Such increasing of image sharpness is proposed to achieve by using the Laplace operator (Laplasian calculation) and adding the result to the original image. The value of derivative at each pixel of the image depends linearly on sharpness level. Thus, it allows separating the areas with abrupt colour changes and gaps from the areas where the brightness is constant or changes slowly. The result of second derivative is much more for the areas with sharp changes than for the areas without them. The areas with constant or slowly-changing brightness after the second derivative calculation become almost the same dark colour. These areas could be restored to original image with retention of sharpness increasing effect. For this, transformed by Laplasian image should be added to the original one. Use of Lapla-sian allows to get an acceptable balance between the speed and computational complexity of the fingerprint recognition algorithm. The technical implementation of the device and illustration of its operation are given. Fingerprints image processing system is executed on the base of STM32f407 microcontroller with CortexM core. The system includes capasitive scanner, TFT LCD display and lab power source. The microcontroller software realizes, in particular, interpolation and contrast increasing. The system is module-compatible and able for scaling

A Novel Convolutional Neural Network Pore-Based Fingerprint Recognition System

Biometrics play an important role in security measures, such as border control and online transactions, relying on traits like uniqueness and permanence. Among the different biometrics, the fingerprint stands out for their enduring nature and individual uniqueness. Fingerprint recognition systems traditionally rely on ridge patterns (Level 1) and minutiae (Level 2). However, these systems suffer from recognition accuracy with partial fingerprints. Level 3 features, such as pores, offer distinctive attributes crucial for individual identification, particularly with high-resolution acquisition devices. Moreover, the use of convolutional neural networks (CNNs) has significantly improved the accuracy in automatic feature extraction for biometric recognition. A CNN-based pore fingerprint recognition system consists of two main modules, pore detection and pore feature extraction and matching modules. The first module generates pixel intensity maps to determine the pore centroids, while the second module extracts relevant features of pores to generate pore representations for matching between query and template fingerprints. However, existing CNN architectures lack in generating deep-level discriminative feature and computational efficiency. Moreover, available knowledge on the pores has not been taken into consideration optimally for pore centroids and metrics other than Euclidean distance have not been explored for pore matching. The objective of this research is to develop a CNN-based pore fingerprint recognition scheme that is capable of providing a low-complexity and high-accuracy performance. The design of the CNN architecture of the two modules aimed at generating features at different hierarchical levels in residual frameworks and fusing them to produce comprehensive sets of discriminative features. Depthwise and depthwise separable convolution operations are judiciously used to keep the complexity of networks low. In the proposed pore centroid part, the knowledge of the variation of the pore characteristics is used. In the proposed pore matching scheme, a composite metric, encompassing the Euclidean distance, angle, and magnitudes difference between the vectors of pore representations, is proposed to measure the similarity between the pores in the query and template images. Extensive experiments are performed on fingerprint images from the benchmark PolyU High-Resolution-Fingerprint dataset to demonstrate the effectiveness of the various strategies developed and used in the proposed scheme for fingerprint recognition