AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

Incomplete fingerprint recognition based on feature fusion and pattern entropy

Considering the inherent characteristics of incomplete fingerprint： local feature loss and global information distortion, the recognition progress has been mainly restricted by two critical problems： how to precisely extract informative features and still with compact representation of the incomplete fingerprint; and how to effectively measure the similarity between fingerprint images. In this paper, to handle the first problem, both the minutiae and orientation field feature are extracted and then fused to get a more comprehensive feature with scale and rotation invariability. Dealing with the second one, the pattern entropy is introduced to robustly measure the similarity of two incomplete fingerprints. Extensive experiments have been conducted on both those popular fingerprint databases and our extended databases containing more incomplete fingerprints. Meanwhile, thorough performance comparisons have been made with existing approaches. Experimental results show that our approach has more efficient ability especially in incomplete fingerprint recognition, and also performs well in both accuracy and efficiency.

Self-powered flexible fingerprint-recognition display based on a triboelectric nanogenerator

In the time of Internet of Things(IoT),alternating current electroluminescence(ACEL)has unique advantages in the fields of smart display and human–computer interaction.However,their reliance on external high-voltage AC power supplies poses challenges in terms of wearability and limits their practical application.This paper proposed an innovative scheme for preparing a feather triboelectric nanogenerator(F-TENG)using recyclable and environmentally friendly material.The highest open-circuit voltage,short-circuit current,and transferred charge of SF6-treated F-TENGs can reach 449 V,63μA,and 152 nC,which enables easy lighting of BaTiO_(3)^(-)doped ACEL devices.Using a human electrical potential,a single-electrode F-TENG is combined with ACEL device for self-powered fingerprint recognition display.These works achieve self-powered flexible wearable ACEL devices,which are not only efficient and portable but also have good application prospects in the human–computer interaction,functional displays,and wearable electronic devices.

Fingerprint Feature Extraction Based on Macroscopic Curvature

In the Automatic Fingerprint Identification Syst e m (AFIS), extracting the feature of fingerprint is very important. The local cur vature of ridges of fingerprint is irregular, so people have the barrier to effe ctively extract the fingerprint curve features to describe fingerprint. This art icle proposes a novel algorithm; it embraces information of few nearby fingerpri nt ridges to extract a new characteristic which can describe the curvature featu re of fingerprint. Experimental results show the algorithm is feasible, and the characteristics extracted by it can clearly show the inner macroscopic curve pro perties of fingerprint. The result also shows that this kind of characteristic i s robust to noise and pollution.

Highly crystallization-induced emissive luminophores with mechanoluminescent features for two-photon harvesting fluorescence imaging and latent fingerprint identification

Fluorescence imaging can be employed in fields of medical treatment,astronomical exploration,and national defense security.Traditional fluorescence imaging often takes the single-photon techniques,which is vulnerable to background interference and photobleaching.Remedially,two-photon fluorescence imaging can achieve much higher-resolution fluorescence imaging for reducing scattering and deeper depth.Hence,by assembling the tetraphenylethylene backbones with nontoxic and non-noble K^(+)ions,compound 1([(Hdma)K(H_(2)ettc)]_(n),H_(4)ettc=4',4''',4''''',4'''''''-(ethene-1,1,2,2-tetrayl)tetrakis(([1,1'-biphenyl]-4-carboxylic acid)))with the crystallization-induced emissions exhibited charming fluorescence imaging under two-photon excitation microscopy(TPEM).Besides,luminescent powders based on compound 1 can achieve high-resolution fingerprint recognition,providing secure access control and identification for a novel authentication method.Compared with the commercial fluorescent dyes coumarin-6,the as-synthesized compound 1 showed great solvent stability,indicating its durability against harsh environment.Moreover,compound 1 shows mechanoluminescent properties for the perturbation of weak supramolecular interactions within ordered arrangements of the H_(2)ettc^(2−)ligands.This novel compound has provided an important insight to the development of twophoton fluorescence imaging and advanced external-stimuli responsive materials.

Fingerprint-Based Identity Authentication and Digital Media Protection in Network Environment

Current information security techniques based on cryptography are facing a challenge of lacking the exact connection between cryptographic key and legitimate users. Biometrics, which refers to distinctive physiological and behavioral characteristics of human beings, is a more reliable indicator of identity than traditional authentication system such as passwords-based or tokens-based. However, researches on the seamless integration biometric technologies, e.g., fingerprint recognition, with cryptosystem have not been conducted until recent years. In this paper, we provide an overview of recent advancements in fingerprint recognition algorithm with a special focus on the enhancement of low-quality fingerprints and the matching of the distorted fingerprint images, and discuss two representative methods of key release and key generation scheme based on fingerprints. We also propose two solutions for the application in identity authentication without trustworthy thirdparty in the network environment, and application in digital media protection, aiming to assure the secrecy of fingerprint template and fingerprint-based user authentication.

Fingerprint matching,spoof and liveness detection:classification and literature review

Fingerprint matching,spoof mitigation and liveness detection are the trendiest biometric techniques,mostly because of their stability through life,uniqueness and their least risk of invasion.In recent decade,several techniques are presented to address these challenges over well-known data-sets.This study provides a comprehensive review on the fingerprint algorithms and techniques which have been published in the last few decades.It divides the research on fingerprint into nine different approaches including feature based,fuzzy logic,holistic,image enhancement,latent,conventional machine learning,deep learning,template matching and miscellaneous tech-niques.Among these,deep learning approach has outperformed other approaches and gained significant attention for future research.By reviewing fingerprint literature,it is historically divided into four eras based on 106 referred papers and their cumulative citations.