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segmentation algorithms are discussed, each having a different accuracy of segmenting fingerprint image. This paper concludes the critical review of fingerprint segmentation, with all the advantages, limitations and complexities of algorithms.

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background.

An important step in an automatic finger-print recognition system is the segmentation of fingerprint images. The task of a fingerprint segmentation algorithm is to decide which part of the image ...

Recognition is the final goal of fingerprint-related tasks, and the design of fingerprint enhancement algorithms needs to consider the matching process. Minutiae-based matching methods are currently the predominant identification technique, and many enhancement networks utilize minutiae information to guide network training.

In automatic fingerprint identification systems, it is crucial to segment the fingerprint images. Inspired by the superiority of convolutional neural networks for various classification and regression tasks, we approach fingerprint segmentation as a binary...

Abstract. A critical step in automatic fingerprint recognition is the accurate seg-mentation of fingerprint images. The objective of fingerprint segmentation is to decide which part of the images belongs to the foreground containing features for recognition and identification, and which part to the background with the noisy area around the boundary of the image. Unsupervised algorithms extract ...

This paper provides an overview of the state of the art techniques of fingerprint image segmentation and contribution of other researchers on segmentation. This paper also discusses a different class of segmentation algorithms with its measuring parameters, computational complexity, advantages, limitations, and applications.

Fingerprint Recognition: Enhancement, Feature Extraction and Automatic Evaluation of Algorithms. A.M. Bazen and S.H. Gerez, "Systematic methods for the computation of the directional fields and singular points of fingerprints," in IEEE tPAMI, July 2002

The proposed algorithm is based on the existed dynamic image segmentation algorithm using fuzzy c-means (FCM) and genetic algorithm. Specifically, relying on different gray level of histogram and improved post-processing method, we establish a well-performed fingerprint segmentation system.

Fingerprint matching plays a crucial role in various security applications, such as identity verification and criminal investigations. While most fingerprint matching systems rely on large machine learning models and sophisticated algorithms, it is also possible to perform this task with simpler, more accessible techniques.

Fingerprints have been widely used for personal recognition in many forensic applications. The segmentation of fingerprint images is a fundamental step in recognition systems. It classifies pixels of the image into two classes the foreground and the background. This paper proposes an improved method for fingerprint segmentation using a histogram-based thresholding approach. The main idea is to ...

To address this, we propose a method that integrates deep learning with feature descriptors for partial fingerprint matching. Specifically, our approach employs a Siamese Network based on a CNN architecture for deep learning, complemented by a SIFT-based feature descriptor to extract minimal yet significant features from the partial fingerprint.

Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and discard many spurious minutiae. In this paper, a new segmentation algorithm is presented.

These constraints are necessary for effective usage of slap fingerprint in a personal authentication system. This paper proposes an algorithm which segments slap fingerprint image accurately, detects hand and fingerprint in near real time.

Fingerprint segmentation is, therefore, a key first step toward fingerprint acquisition and recognition. . Automated fingerprint segmentation . Algorithms for fingerprint segmentation, therefore, need to consider both the acquisition conditions, in order to be able to include as much information as possible.

Contactless fingerprint recognition systems offer a hygienic, user-friendly, and efficient alternative to traditional contact-based methods. However, their accuracy heavily relies on precise fingertip detection and segmentation, particularly under challenging background conditions. This paper introd …

quite frequently used in various fingerprint algorithms and techniques. The approach mainly involves extraction of minutiae points from the sample fingerprint images and then performing fingerprint matching based on the number of minutiae pairings among two fingerprints in question. Keywords: Image Segmentation; Minutiae; fingerprint.

Radio Frequency Fingerprint Identification (RFFI), which leverages hardware-specific impairments in Internet of Things (IoT) devices, is widely used for device authentication and spoofing attack detection to enhance communication security. However, the existing RFFI methods heavily depend on large-scale training datasets in deep learning (DL), with severe overfitting issues if the training ...

With all these concepts and algorithms together we completed our project, which helps us to detect the different segments of our fingerprint image. However, we can apply this script to more images, and the results are quite pretty by changing the color pallete:

The segmentation algorithm has been trained on fingerprints of this database, but not on these particular fingerprints. Human inspection shows that the block-wise coherence algorithm provides satisfactory result. Keyword: fingerprint image segmentation, block-wise, coherence, minutiae, singular point. 1.