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

Enhanced Fuzzy Feature Match Algorithm for Mehndi Fingerprints

The performance of biometric system is degraded by the distortions occurred in finger print image acquisition. This paper focuses on nonlinear distortions occurred due to �Mehndi / Heena drawn on the palm/fingers. The present invention is to detect and rectify such distortions using feedback paradigm. If image is of good quality, there is no need to renovate features. So, quality of whole image is checked by generating exponential similarity distribution. Quality of local region is checked by the ridge continuity map and ridge clarity map. Then, we check whether feedback is needed or not. The desired features such as ridge structure, minutiae point, orientation, etc. are renovated using feedback paradigm. Feedback is taken from top K matched template fingerprints registered in the database. Fuzzy logic handles uncertainties and imperfections in images. For matching, we have proposed the Enhanced Fuzzy Feature Match (EFFM) for estimating triangular feature set of distance between minutiae, orientation angle of minutiae, angle between the direction of minutiae points, angle between the interior bisector of triangle and the direction of minutiae, and a minutiae type. The proposed algorithm incorporates an additional parameter minutiae type that assists to improve accuracy of matching algorithm. The experimentation on 300 Mehndi fingerprints acquired using Secugen fingerprint scanner is conducted. The results positively support EEFM for its efficiency and reliability to handle distorted fingerprints matching

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.Comment: Accepted in the IEEE Transactions on Information Forensics and Securit