**1. Introduction**

Various approaches to automatically authenticate fingerprints for personal identification and verification have found important applications in ensuring public security and criminal investigations. A fingerprint represents a graphical pattern on the surface of a human finger expressed by ridges and valleys. Ridges are the upper skin surface parts of the finger that touch a surface, and valleys are the lower parts. In a fingerprint image, ridge lines are the dark areas, and valleys are the bright areas which represent the inter-ridge spaces. Fingerprints are unique and are the most reliable human feature which can be utilized for personal identification [1].

Automatic fingerprint identification uses fingerprint features such as ridge flow, ridge period, ridge ending, and the delta or core points for fingerprint enrollment and verification steps [2]. Matching performance is strongly affected by fingertip surface conditions such

**Citation:** Dinc ˘a L˘az ˘arescu, A.-M.; Moldovanu, S.; Moraru, L. A Fingerprint Matching Algorithm Using the Combination of Edge Features and Convolution Neural Networks. *Inventions* **2022**, *7*, 39. https://doi.org/10.3390/ inventions7020039

Academic Editor: Anastasios Doulamis

Received: 10 May 2022 Accepted: 26 May 2022 Published: 27 May 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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as fingerprint deformations or distortions, fingerprint collection conditions, variations in the pressure between the finger and the acquisition sensor, scars, age, race, sex, etc. Additionally, the minutiae extraction can be affected by noise, rotation, and the scale of the images or fingerprint alignment information [1–3]. There is a sinusoidal-shaped wave of ridges with some slow changes in their orientation. This characteristic defines a fingerprint pattern. However, fingerprint images are prone to structural imperfections. In order to create an accurate identification system, an effective enhancement algorithm is necessary. This algorithm can be coupled with a performance classification method [4,5]. A major limitation of fingerprint recognition algorithms is that only small-area fingerprint images are usually available to the algorithm for differential matching. This calls for a model which can solve the restoration of the whole fingerprint image to make the process of fingerprint recognition and matching more effective [6–8]. The enhancement step is based on the obvious directional behavior manifested in a fingerprint image. Some effective enhancement techniques are based on the Prewitt and Laplacian of Gaussian filters [9,10]. Additionally, a robust feature extractor and classifier must be able to deal with augmentation operations such as translation, rotation, or skin distortion.

The process of feature extraction and matching demands some preprocessing operations such as ridge enhancement (for a fingerprint structure clarity), followed by feature extraction using artificial neural networks. Recently, deep convolutional networks have been heavily used in image recognition. Most of these are devoted to a single-frame recognition with an improved classification performance [11–15]. The main advantage of CNN-based classifiers is that they are fully independent of any human actions devoted to feature extraction and classification. For large databases, the computational cost of searching for a fingerprint image is huge, but CNNs drastically reduce this burden.

In the present study, starting from the fact that the existing fingerprint recognition algorithms rely too heavily on the details of the fingerprint, a software solution was proposed to evaluate the quality of fingerprint identification by using a convolutional neural network (CNN) architecture and by calling full images belonging to four digital databases. We did not perform any handcrafted feature extraction operations. The main challenge that we face is the low quality of fingerprints, which can impede or make the identification process difficult.

The main contributions of this work are as follows:


The rest of this paper is organized as follows: Section 2 reviews the related literature and emphasizes the most important sources of our motivation. Also, in Section 2

the adopted methodology, the design of our CNN method, and the used databases are presented. Section 3 shows the results obtained from several experiments, discusses and evaluates the accuracy values provided by the individual systems. Finally, Section 4 provides a summary of our research and sets out our future work intentions.
