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Applying Ateb–Gabor Filters to Biometric Imaging Problems

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Applying Ateb–Gabor Filters to Biometric Imaging Problems S S symmetry Article Applying Ateb–Gabor Filters to Biometric Imaging Problems Mariia Nazarkevych 1 , Natalia Kryvinska 2,* and Yaroslav Voznyi 1 1 Department of Publishing Information Technologies, Lviv Polytechnic National University, 12 Bandera Str., 79000 Lviv, Ukraine; [email protected] (M.N.); [email protected] (Y.V.) 2 Faculty of Management, Comenius University in Bratislava, Odbojárov 10, 820 05 Bratislava, Slovakia * Correspondence: [email protected] Abstract: This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well. Keywords: wavelet-Ateb–Gabor transformation; filtration; biometric images Citation: Nazarkevych, M.; Kryvinska, N.; Voznyi, Y. Applying Ateb–Gabor Filters to Biometric 1. Introduction Imaging Problems. Symmetry 2021, 13, The field of biometric identification systems (BISs) is developing quickly [1]. These sys- 717. https://doi.org/10.3390/ tems attract special attention due to their high reliability and recent significant price reduc- sym13040717 tions, and are often used for real-time recognition tasks [2]. Biometric security systems are gradually displacing traditional security systems Academic Editor: Dumitru Baleanu based on the entry of passwords, PIN codes, which are personal identification num- bers, and printed IDs. Systems of this kind are an identification technology with various Received: 21 March 2021 means of protection and are used to compare the obtained physiological or behavioral Accepted: 13 April 2021 characteristics of a particular person. Published: 19 April 2021 The development of biometrics is the result of the wide usage of embedded miniature sensors in small personal devices such as smartphones, deployment of biometric data in Publisher’s Note: MDPI stays neutral various data systems and large-scale identification, and study of usage of biometric features with regard to jurisdictional claims in in wearable devices [3]. published maps and institutional affil- It is a common knowledge, that almost every security biometric system implicitly iations. imposes a certain type of restriction on its users. Such systems require the creation of a certain type of environment during data collection. It is also important to allocate enough memory to store the collected information. However, cheaper biometric sensors and the rapid development of smartphones, smart watches, and various other devices provide Copyright: © 2021 by the authors. wider opportunities for researchers. Thus, today, we observe a revolution in devices with Licensee MDPI, Basel, Switzerland. biometric logins. This article is an open access article Such devices store the personal information of the owner of the device or person who distributed under the terms and has the right to use this device. These devices that enable biometric authentication are conditions of the Creative Commons equipped with biometric sensors and are able to constantly check the owner of the certain Attribution (CC BY) license (https:// biometric data. Therefore, these attributes of the individuals can be used as a biometric creativecommons.org/licenses/by/ 4.0/). signature of the person. Symmetry 2021, 13, 717. https://doi.org/10.3390/sym13040717 https://www.mdpi.com/journal/symmetry Symmetry 2021, 13, 717 2 of 17 In [4], biometric images are viewed from different angles and different orientations. They are filtered by the Gabor function. The system is invariant for the rotation of the face of the profile in one or different lanes. Based on that fact, we can claim that Gabor filtration is promising. In [5], it is established a secure and reliable algorithm for distinguishing fingerprints from counterfeits, using circular Gabor Wavelet to segment the textures of captured images. The samples are subjected to extraction functions using a circular Gabor wavelet algorithm designed for segmentation. The enhancement of the biometric images is extremely essential during the deploy- ment of automated fingerprint identification systems. In [6], an authentication method based on a new lighting compensation scheme when scanning fingerprints was developed. The proposed compensation scheme involves a new method of light decomposition and compensation of external lighting. Such approach enables the scholars to decently im- prove the image quality. The results of the experiments show that the image quality after applying the adaptive higher-order singular value decomposition on a tensor of wavelet subbands of a fingerprint (AHTWF) method is higher than the quality of the original images. The AHTWF method is recommended to apply to fingerprint images that have ridge and concavity structures, and also contain a lot of blurred areas. As the outputs of the fingerprint scanning can often be of the low quality, the AHTWF method is proposed to highly improve the higher accuracy of fingerprint classification. Multimodal biometrics is mainly used for certification and identity verification. Many bio- metric data are used for human authentication. Biometric recognition involves three vital phases, which include preprocessing, trait detection, and classification. Thus, preprocessing included using filters and image cropping, texture overlay, and shape creation [7]. Today, the security of personal information is very important. A huge amount of per- sonal data and files are stored on servers with closed access. Access to private information is provided only to authorized users. Biometrics plays a big role in security. Nowadays, spe- cialists have begun to study the images of finger veins very actively. The great advantage of vein patterns on the fingers is that they cannot be copied or stolen. The use of basic fingers for authenticity is widely used in Japan as a database at airports, identity authentication, prisons, etc. The main attention is paid to the comparative study of existing methods of pattern recognition of veins, the technique of creating features [8]. A method based on the Gabor filter is introduced to extract fingerprint functions actively from images without pre-processing. The proposed method is more efficient than conventional methods for a small fingerprint recognition system. The results of experiments claim that the recognition coefficient of the k-nearest classifier of neighbors using the proposed functions based on the Gabor filter is 97.2% [9]. Improving image prints is an important step in pre-processing in fingerprint recogni- tion programs. In [10], they proposed a new filter design method to improve the fingerprint image, mainly the traditional Gabor filter (TGF). Its parameter selection scheme does not depend on the image. The advantages are based on maintaining the structure of the fingerprint image and achieving a sequence of image enhancement. In [10], the Log-Gabor method is developed. It is used to solve the issue of im- proving fingerprint recognition and widen the usage of common Gabor filter. The pre- sented method is important for the reliability of identification and other issues related to the fingerprint recognition. The Gabor filter can be divided into a band-pass filter and a low-pass filter. The orien- tation aspect is introduced to implement the quicker Gabor filter processing. The division into a band-pass filter and a low-pass filter enables to significantly reduce the compu- tational complexity, by 1:2.8 times. Thus, the filter execution time decreases by (1:1.46), memory space lessens, and biometric image quality significantly improves compared to a traditional Gabor filter [11]. The works of Shelupanov [12] and Bidyuk [13] are devoted to the problem of biometric identification in which special attention is paid to the control and security of the system. Symmetry 2021, 13, 717 3 of 17 The work [14], where new approach to the protection of information systems is studied, deserves a special attention. In the works of [15], they considered attacks on biometric protection systems. A study [16] showed that biometric protection depends on many factors that will shape security pol- icy. Among foreign researchers, it is worth noting the works of Vacca [17], Nixon [18], and Nanavati [19]. The main contribution of this paper can be summarized as follows: • The symmetry of Ateb–Gabor functions enables us to create a great variety and amount of filters, that differ in form and size. All of them supplement Gabor function. • The basis of wavelet-Ateb–Gabor functions allows us to create a large number of different filters that will effectively convert images into a skeleton and provide fast
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