Relevant bibliographies by topics / Visual fingerprint

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Visual fingerprint'

Author: Grafiati
Published: 22 February 2025

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Journal articles on the topic "Visual fingerprint"

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Anshari, Muhammad, Mitra istiar Wardhana, and Dhara Alim Cendekia. "Visual Login Fingerprints Scanner Aplikasi Mobile Banking (BRImo, Jenius, BNI Mobile Banking) berdasarkan Model Kait Nir Eyal." JoLLA: Journal of Language, Literature, and Arts 3, no. 8 (August 31, 2023): 1198–216. http://dx.doi.org/10.17977/um064v3i82023p1198-1216.

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Fitur fingerprint scanner cenderung lebih sering digunaakan secara repetitif untuk login pada aplikasi perbankan karena dianggap lebih cepat dan mudah. Anggapan ini juga terlihat dari sedikit­nya usaha kognitif pada proses pencarian tombol fingerprint scanner. Tombol fingerprint scanner aplikasi BRImo, BNI Mobile Banking dan Jenius cenderung menjadi kontras serta halaman login di­desain untuk mengarahkan pengguna pada fitur tersebut. Berdasarkan kebiasaan repetitif peng­gunaan fingerprint scanner dan aspek visual yang mengarahkan pengguna ke fingerprint scanner penelitaia ini menggunakan model kait untuk menguraikan cara visual fitur tersebut berefek pada penggunaan yang repetitif ketika. Hasil dari penelitian ini menyebutkan bahawa aplikasi BRImo yang menggunakan kontras visual pada tombol fingerprint scanner yang juga secara implisit mem­berikan kontras secara fungsi pada tombol disekitarya bila ditinjau berdasarkan model kait bisa memicu pengguna untuk menekan tombol tersebut secara repetitif. Kata kunci: model Kait; aplikasi perbankan; fingerprint scanner Visual Login Fingerprints Scanner for Mobile Banking Applications (BRImo, Jenius, BNI Mobile Banking) based on the Kait Nir Eyal Model The fingerprint scanner feature tends to be used repeatedly to log in to banking applications because it is considered faster and easier. This assumption is also seen from the lack of cognitive effort in the process of searching for the fingerprint scanner button. The fingerprint scanner buttons for the BRImo, BNI Mobile Banking and Jenius applications tend to be in contrast and the login page is designed to direct users to these features. Based on the repetitive habit of using fingerprint scanners and the visual aspects that direct users to the fingerprint scanner, this study uses a hook model to describe how visually these features have an effect on repetitive use. The results of this study indicate that the BRImo application that uses visual contrast on the fingerprint scanner button which also implicitly provides functional contrast to the surrounding buttons when viewed based on the latch model can trigger the user to press the button repeatedly. Keywords: Kait model; internet banking; fingerprint scanner
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Zhang, Huiqing, and Yueqing Li. "LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints." Sensors 21, no. 11 (May 25, 2021): 3662. http://dx.doi.org/10.3390/s21113662.

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Smartphones are increasingly becoming an efficient platform for solving indoor positioning problems. Fingerprint-based positioning methods are popular because of the wide deployment of wireless local area networks in indoor environments and the lack of model propagation paths. However, Wi-Fi fingerprint information is singular, and its positioning accuracy is typically 2–10 m; thus, it struggles to meet the requirements of high-precision indoor positioning. Therefore, this paper proposes a positioning algorithm that combines Wi-Fi fingerprints and visual information to generate fingerprints. The algorithm involves two steps: merged-fingerprint generation and fingerprint positioning. In the merged-fingerprint generation stage, the kernel principal component analysis feature of the Wi-Fi fingerprint and the local binary pattern features of the scene image are fused. In the fingerprint positioning stage, a light gradient boosting machine (LightGBM) is trained with mutually exclusive feature bundling and histogram optimization to obtain an accurate positioning model. The method is tested in an actual environment. The experimental results show that the positioning accuracy of the LightGBM method is 90% within a range of 1.53 m. Compared with the single-fingerprint positioning method, the accuracy is improved by more than 20%, and the performance is improved by more than 15% compared with other methods. The average locating error is 0.78 m.
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Popov, Vladimir. "The Problem of Selection of Fingerprints for Topological Localization." Applied Mechanics and Materials 365-366 (August 2013): 946–49. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.946.

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Visual navigation is extensively used in contemporary robotics. In particular, we can mention different systems of visual landmarks. In this paper, we consider one-dimensional color panoramas. Panoramas can be used for creating fingerprints. Fingerprints give us unique identifiers for visually distinct locations by recovering statistically significant features. Fingerprints can be used as visual landmarks for mobile robot navigation. In this paper, we consider a method for automatic generation of fingerprints. Since a fingerprint is a circular string, different string-matching algorithms can be used for selection of fingerprints. In particular, we consider the problem of finding the consensus of circular strings under the Hamming distance metric.
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Luda, M. P., N. Li Pira, D. Trevisan, and V. Pau. "Evaluation of Antifingerprint Properties of Plastic Surfaces Used in Automotive Components." International Journal of Polymer Science 2018 (November 28, 2018): 1–11. http://dx.doi.org/10.1155/2018/1895683.

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The antifingerprint properties of a range of surfaces produced with different technologies (in-mould decoration, in-mould labeling, and painted) were objectively evaluated by depositing on them in standard conditions an artificial fingerprint for direct determination of its visibility. The artificial fingerprint behaves similarly to the real human fingerprints. A classification method is then proposed to classify surfaces on the base of antifingerprint properties by measuring the roughness profile (Ra) and calculating the % variation of gloss (GU 20 and 60°), haze, luminance (L), and diffuse reflectance (R) values after fingerprint deposition. This approach provides an objective and quantitative test method to determine visual antifingerprint properties of coated surfaces, instead of the “easy-to-clean” properties commonly evaluated. The data acquired provides a design guideline for fabricating visually fingerprint-free surfaces by controlling roughness, texture, color, and transparency of surfaces, with the aim of optically masking fingerprints.
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Shams, Haroon, Tariqullah Jan, Amjad Ali Khalil, Naveed Ahmad, Abid Munir, and Ruhul Amin Khalil. "Fingerprint image enhancement using multiple filters." PeerJ Computer Science 9 (January 3, 2023): e1183. http://dx.doi.org/10.7717/peerj-cs.1183.

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Biometrics is the measurement of an individual’s distinctive physical and behavioral characteristics. In comparison to traditional token-based or knowledge-based forms of identification, biometrics such as fingerprints, are more reliable. Fingerprint images recorded digitally can be affected by scanner noise, incorrect finger pressure, condition of the finger’s skin (wet, dry, or abraded), or physical material it is scanned from. Image enhancement algorithms applied to fingerprint images remove noise elements while retaining relevant structures (ridges, valleys) and help in the detection of fingerprint features (minutiae). Amongst the most common image enhancement filters is the Gabor filter, however, given their restricted maximum bandwidth as well as limited range of spectral information, it falls short. We put forward a novel method of fingerprint image enhancement using a combination of a diffusion-coherence filter and a 2D log-Gabor filter. The log-Gabor overcomes the limitations of the Gabor filter while Coherence Diffusion mitigates noise elements within fingerprint images. Implementation is done on the FVC image database and assessed via visual comparison with coherence diffusion used disjointedly and with the Gabor filter.
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Zabala-Blanco, David, Marco Mora, Ricardo J. Barrientos, Ruber Hernández-García, and José Naranjo-Torres. "Fingerprint Classification through Standard and Weighted Extreme Learning Machines." Applied Sciences 10, no. 12 (June 15, 2020): 4125. http://dx.doi.org/10.3390/app10124125.

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Fingerprint classification is a stage of biometric identification systems that aims to group fingerprints and reduce search times and computational complexity in the databases of fingerprints. The most recent works on this problem propose methods based on deep convolutional neural networks (CNNs) by adopting fingerprint images as inputs. These networks have achieved high classification performances, but with a high computational cost in the network training process, even by using high-performance computing techniques. In this paper, we introduce a novel fingerprint classification approach based on feature extractor models, and basic and modified extreme learning machines (ELMs), being the first time that this approach is adopted. The weighted ELMs naturally address the problem of unbalanced data, such as fingerprint databases. Some of the best and most recent extractors (Capelli02, Hong08, and Liu10), which are based on the most relevant visual characteristics of the fingerprint image, are considered. Considering the unbalanced classes for fingerprint identification schemes, we optimize the ELMs (standard, original weighted, and decay weighted) in terms of the geometric mean by estimating their hyper-parameters (regularization parameter, number of hidden neurons, and decay parameter). At the same time, the classic accuracy and penetration-rate metrics are computed for comparison purposes with the superior CNN-based methods reported in the literature. The experimental results show that weighted ELM with the presence of the golden-ratio in the weighted matrix (W-ELM2) overall outperforms the rest of the ELMs. The combination of the Hong08 extractor and W-ELM2 competes with CNNs in terms of the fingerprint classification efficacy, but the ELMs-based methods have been demonstrated their extremely fast training speeds in any context.
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Makrushin, Andrey, Venkata Srinath Mannam, and Jana Dittmann. "Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms." Applied Sciences 13, no. 18 (September 5, 2023): 10000. http://dx.doi.org/10.3390/app131810000.

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The datasets of synthetic biometric samples are created having in mind two major objectives: bypassing privacy concerns and compensating for missing sample variability in datasets of real biometric samples. If the purpose of generating samples is the evaluation of biometric systems, the foremost challenge is to generate so-called mated impressions—different fingerprints of the same finger. Note that for fingerprints, the finger’s identity is given by the co-location of minutiae points. The other challenge is to ensure the realism of generated samples. We solve both challenges by reconstructing fingerprints from pseudo-random minutiae making use of the pix2pix network. For controlling the identity of mated impressions, we derive the locations and orientations of minutiae from randomly created non-realistic synthetic fingerprints and slightly modify them in an identity-preserving way. Our previously trained pix2pix models reconstruct fingerprint images from minutiae maps, ensuring that the realistic appearance is transferred from training to synthetic samples. The main contribution of this work lies in creating and making public two synthetic fingerprint datasets of 500 virtual subjects with 8 fingers each and 10 impressions per finger, totaling 40,000 samples in each dataset. Our synthetic datasets are designed to possess characteristics of real biometric datasets. Thus, we believe they can be applied for the privacy-friendly testing of fingerprint recognition systems. In our evaluation, we use NFIQ2 for approving the visual quality and Verifinger SDK for measuring the reconstruction success.
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Yadav, Nisha, Deeksha Mudgal, Amarnath Mishra, Sacheendra Shukla, Tabarak Malik, and Vivek Mishra. "Harnessing fluorescent carbon quantum dots from natural resource for advancing sweat latent fingerprint recognition with machine learning algorithms for enhanced human identification." PLOS ONE 19, no. 1 (January 4, 2024): e0296270. http://dx.doi.org/10.1371/journal.pone.0296270.

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Nowadays, it is fascinating to engineer waste biomass into functional valuable nanomaterials. We investigate the production of hetero-atom doped carbon quantum dots (N-S@MCDs) to address the adaptability constraint in green precursors concerning the contents of the green precursors i.e., Tagetes erecta (marigold extract). The successful formation of N-S@MCDs as described has been validated by distinct analytical characterizations. As synthesized N-S@MCDs successfully incorporated on corn-starch powder, providing a nano-carbogenic fingerprint powder composition (N-S@MCDs/corn-starch phosphors). N-S@MCDs imparts astounding color-tunability which enables highly fluorescent fingerprint pattern developed on different non-porous surfaces along with immediate visual enhancement under UV-light, revealing a bright sharp fingerprint, along with long-time preservation of developed fingerprints. The creation and comparison of latent fingerprints (LFPs) are two key research in the recognition and detection of LFPs, respectively. In this work, developed fingerprints are regulated with an artificial intelligence program. The optimum sample has a very high degree of similarity with the standard control, as shown by the program’s good matching score (86.94%) for the optimal sample. Hence, our results far outperform the benchmark attained using the conventional method, making the N-S@MCDs/corn-starch phosphors and the digital processing program suitable for use in real-world scenarios.
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Hasoun, Rajaa, Soukaena Hashem, and Rehab Hasan. "A Proposed Hybrid Fingerprint, Image Fusion and Visual Cryptography Technique for Anti-Phishing." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (October 10, 2021): 328–48. http://dx.doi.org/10.55562/jrucs.v39i1.216.

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This paper proposes an anti-phishing web site system it is carried out by the two following stages: Registration phase; the user enters username and password then (his/her) fingerprint, server site selects virtual fingerprint image. The fusion will be applied to fuse real fingerprint with virtual one, fused image will be input to visual cryptography(VC) scheme to produce two shares, one share kept with user in addition to fuse image, and other shares are kept with the server. Authentication phase; in this phase the user enters the password and is asked to enter the fingerprint. Pattern recognition is done to determine if it is hacker or authenticated user, when the server accepts the fingerprint the user will be required to input (his/her) share, so the user share is stacked with server share and generated image is displayed. The user will decide if it’s a phishing site or not depending on the displayed image (after matching it with the image that the server shared through registration phase).From many experimental works conducted on the proposal, we notice the strength is centered in image fusion. Where the fused fingerprint images have higher quality (entropy) than the single fingerprint image, that increases randomness of the VC shares which are extracted from the fused fingerprint.
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Wu, Feng, and Baohua Jiang. "Application of Fluorescent Carbon Nanoelectronic Materials in Combining Partial Differential Equations for Fingerprint Development and Its Image Enhancement." Journal of Nanoelectronics and Optoelectronics 18, no. 9 (September 1, 2023): 1070–77. http://dx.doi.org/10.1166/jno.2023.3496.

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Fluorescent developers play a crucial role when dealing with objects with complex patterns or color interference against their background due to their excellent photoluminescent properties. In recent years, fluorescent nanoelectronic materials have emerged as a novel class of fluorescent materials for fingerprint development research. Here, carbon quantum dots (CQDs) are synthesized using an electrochemical method and utilized as fluorescent nanoelectronic materials in combination with gold nanoparticles (AuNPs) to form a composite (Au/CQDs). The photoelectric properties of Au/CQDs are verified, and a precursor solution for Au/CQDs is prepared using the Wessling method. After the addition of a surfactant and subsequent elimination processes, an Au/CQDs fluorescent nanocolloidal solution is obtained. This solution is applied for the development of visible fingerprints and latent fingerprints on adhesive surfaces. The resulting development images are subjected to enhancement processes such as sharpening, smoothing, and noise reduction using partial differential equations to improve their visual quality. In experiments, under light exposure, Au/CQDs exhibit a higher conversion rate with cyclohexane compared to conditions without light. In the Au/NPs system, the fluorescence of CQDs is effectively quenched due to the rapid electron transfer process within the Au/CQDs system. Moreover, the electrode modified with Au/CQDs shows significantly improved efficiency in decomposing H2O2 compared to conditions without light exposure. After the development with Au/CQDs nanoparticle colloid solution, bright fingerprint patterns are visible under ultraviolet light. As the age of the fingerprint increases, the developed fingerprint has a higher resolution than fresh fingerprints. Image enhancement through partial differential equations results in satisfactory sharp edges and smooth contours in the images.
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Dissertations / Theses on the topic "Visual fingerprint"

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Allouche, Mohamed. "Video tracking for marketing applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS033.

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Au cours des dernières décennies, la production et la consommation de vidéos ont considérablement augmenté et il est communément admis que 80 % du trafic Internet est constitué de vidéos. Dans ce cadre, les vidéos de marketing sont encore dominées par le contenu payant (c'est-à-dire le contenu créé par l'annonceur qui paie un annonceur pour distribuer ce contenu). Cependant, le contenu vidéo organique progresse lentement mais sûrement. Le terme « contenu organique » fait référence à un contenu dont la création et/ou la distribution n'est pas payante. Dans la plupart des cas, il s'agit d'un contenu créé par l'utilisateur avec une valeur publicitaire implicite, ou d'un contenu publicitaire distribué par un utilisateur sur un réseau social. En pratique, un tel contenu est directement produit par les appareils de l'utilisateur dans un format compressé (par exemple AVC - Advanced Video Coding, HEVC - High efficiency Video Coding ou VVC - Versatile Video Coding) et est souvent partagé par d'autres utilisateurs, sur le même réseau social ou sur des réseaux sociaux différents, créant ainsi une chaîne virtuelle de distribution qui est étudiée par les experts en marketing.Une telle application peut être modélisée par au moins deux cadres scientifiques différents, à savoir la blockchain et l'empreinte (fingerprinting) vidéo. D'une part, si l'on considère d'abord les problèmes de distribution, la blockchain semble être une solution attrayante, car elle prévoit une solution sécurisée, décentralisée et transparente pour suivre les changements de tout actif numérique. Alors que la blockchain a déjà prouvé son efficacité dans une grande variété d'applications de distribution de contenu, ses applications liées au multimédia restent rares et soulèvent des contradictions conceptuelles entre les ressources de calcul/stockage strictement limitées disponibles dans la blockchain et la grande quantité de données représentant le contenu vidéo ainsi que les opérations complexes que le traitement vidéo exige. D'autre part, si l'on considère d'abord les questions relatives au contenu multimédia, chaque étape de la distribution peut être considérée comme une opération de quasi-doublonnage. Ainsi, le suivi d'une vidéo organique peut être assuré par l'empreinte vidéo qui regroupe les efforts de recherche consacrés à l'identification des versions dupliquées et/ou répliquées d'une séquence vidéo donnée dans un ensemble de données vidéo de référence. Alors que le suivi du contenu vidéo dans le domaine non compressé est un domaine de recherche riche, l'empreinte vidéo dans le domaine compressé est encore sous-explorée.La présente thèse étudie la possibilité de tracer un contenu vidéo compressé publicitaire, dans le contexte de sa propagation spontanée et incontrôlée dans un réseau distribué :• le suivi vidéo au moyen de solutions basées sur la blockchain, malgré la grande quantité de données et les exigences de calcul des applications vidéo, a priori incompatibles avec les solutions blockchain actuelles• le fingerprinting vidéo dans le domaine compressé, même si la compression vidéo est censée exclure la redondance visuelle qui permet de retrouver le contenu vidéo.• les synergies applicatives entre la blockchain et le fingerprinting vidéo.Les principaux résultats consistent en la conception, la spécification et la mise en œuvre de :• COLLATE - une architecture de répartition de charge on-chain off-chain, qui permet d'étendre de manière abstraite les ressources informatiques, de stockage et logicielles intimement limitées de n'importe quelle blockchain par des ressources informatiques à usage général ;• COMMON - Compressed dOMain Marketing videO fiNgerprinting, démontrant la possibilité de modéliser des empreintes vidéo compressées dans un cadre d'apprentissage profond• BIDDING - BlockchaIn-baseD viDeo fINgerprintinG, un pipeline de traitement de bout en bout pour coupler l'empreinte vidéo à la solution d'équilibrage de charge de la blockchain
The last decades have seen video production and consumption rise significantly: TV/cinematography, social networking, digital marketing, and video surveillance incrementally and cumulatively turned video content into the predilection type of data to be exchanged, stored, and processed. It is thus commonly considered that 80% of the Internet traffic is video, and intensive and holistic efforts for devising lossy video compression solutions are carried out to reach the trade-off between video data size and their visual quality.Under this framework, marketing videos are still dominated by the paid content (that is, content created by the advertiser that pays an announcer for distributing that content). Yet, organic video content is slowly but surely advancing. In a nutshell, the term organic content refers to a content whose creation and/or distribution is not paid. In most cases, it is a user-created content with implicit advertising value, or some advertising content distributed by a user on a social network. In practice, such a content is directly produced by the user devices in compressed format (e.g. the AVC - Advanced Video Coding, HEVC - High efficiency Video Coding or VVC - Versatile Video Coding) and is often shared by other users, on the same or on different social networks, thus creating a virtual chain distribution that is studied by marketing experts.Such an application can be modeled by at least two different scientific methodological and technical frameworks, namely blockchain and video fingerprinting. On the one hand, should we first consider the distribution issues, blockchain seems an appealing solution, as it makes provisions for a secure, decentralized, and transparent solution to track changes of any digital asset. While blockchain already proved its effectiveness in a large variety of content distribution applications, its multimedia related applications stay scarce and rise conceptual contradictions between the strictly limited computing/storage resources available in blockchain and the large amount of data representing the video content as well as the complex operations video processing requires. On the other hand, should we first consider the multimedia content issues, each step of distribution can be considered as a near duplication operation. Thus, the tracking of organic video can be ensured by video fingerprinting that regroups research efforts devoted to identifying duplicated and/or replicated versions of a given video sequence in a reference video dataset. While tracking video content in uncompressed domain is a rich research field, compressed domain video fingerprinting is still underexplored.The present thesis studies the possibility of tracking advertising compressed video content, in the context of its uncontrolled, spontaneous propagation into a distributed network:• video tracking by means of blockchain-based solutions, despite the large amount of data and the computation requirements of video applications, a priori incompatible with nowadays blockchain solutions• effective compressed domain video fingerprinting, even though video compression is supposed to exclude the very visual redundancy that allows video content to be retrieved.• applicative synergies between blockchain and fingerprinting frameworks.The main results consist in the conception, specification and implementation of:• COLLATE, an on-Chain Off-chain Load baLancing ArchiTecturE, thus making it possible for the intimately constrained computing, storage and software resources of any blockchain to be abstractly extended by general-purpose computing machine resources;• COMMON - Compressed dOMain Marketing videO fiNgerprinting, demonstrating the possibility of modelling compressed modeling video fingerprint under deep learning framework• BIDDING - BlockchaIn-baseD viDeo fINgerprintinG, an end-to-end processing pipeline for coupling compressed domain video fingerprinting to the blockchain load balancing solution
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Mei, Yuanxun. "Visualization of Wine Attributes." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-6159.

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As the development of the Internet and the rapid increase of data, information visualization is becoming more and more popular. Since human eyes receive visual information very quick and easy, the visualization can make complex and large data more understandable.

Describing sensory perceptions, such as taste, is a challenging task. For a customer, the visualization of the taste of a specific wine together with the other wine attributes such as color and grape type would help him/her choose the right one.    In the thesis, two suitable representations of wine attributes are implemented. And, the final system contains two parts. One is a user interface generating his/her fingerprint based on the two representations. The other one is generating the fingerprints of all wines in a database, and save these fingerprints as images. If the user compares his/her wine fingerpr

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Kasaei, Shohreh. "Fingerprint analysis using wavelet transform with application to compression and feature extraction." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36053/7/36053_Digitised_Thesis.pdf.

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The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.
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Garboan, Adriana. "Traçage de contenu vidéo : une méthode robuste à l’enregistrement en salle de cinéma." Thesis, Paris, ENMP, 2012. http://www.theses.fr/2012ENMP0097/document.

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Composantes sine qua non des contenus multimédias distribués et/ou partagés via un réseau, les techniques de fingerprinting permettent d'identifier tout contenu numérique à l'aide d'une signature (empreinte) de taille réduite, calculée à partir des données d'origine. Cette signature doit être invariante aux transformations du contenu. Pour des vidéos, cela renvoie aussi bien à du filtrage, de la compression, des opérations géométriques (rotation, sélection de sous-région… ) qu'à du sous-échantillonnage spatio-temporel. Dans la pratique, c'est l'enregistrement par caméscope directement dans une salle de projection qui combine de façon non linéaire toutes les transformations pré-citées.Par rapport à l'état de l'art, sous contrainte de robustesse à l'enregistrement en salle de cinéma, trois verrous scientifiques restent à lever : (1) unicité des signatures, (2) appariement mathématique des signatures, (3) scalabilité de la recherche au regard de la dimension de la base de données.La principale contribution de cette thèse est de spécifier, concevoir, implanter et valider TrackART, une nouvelle méthode de traçage des contenus vidéo relevant ces trois défis dans un contexte de traçage de contenus cinématographiques.L'unicité de la signature est obtenue par sélection d'un sous-ensemble de coefficients d'ondelettes, selon un critère statistique de leurs propriétés. La robustesse des signatures aux distorsions lors de l'appariement est garantie par l'introduction d'un test statistique Rho de corrélation. Enfin, la méthode développée est scalable : l'algorithme de localisation met en œuvre une représentation auto-adaptative par sac de mots visuels. TrackART comporte également un mécanisme de synchronisation supplémentaire, capable de corriger automatiquement le jitter introduit par les attaques de désynchronisation variables en temps.La méthode TrackART a été validée dans le cadre d'un partenariat industriel, avec les principaux professionnels de l'industrie cinématographique et avec le concours de la Commission Technique Supérieure de l'Image et du Son. La base de données de référence est constituée de 14 heures de contenu vidéo. La base de données requête correspond à 25 heures de contenu vidéo attaqué, obtenues en appliquant neuf types de distorsion sur le tiers des vidéo de la base de référence.Les performances de la méthode TrackART ont été mesurées objectivement dans un contexte d'enregistrement en salle : la probabilité de fausse alarme est inférieure à 16*10^-6, la probabilité de perte inférieure à 0,041, la précision et le rappel sont égal à 93%. Ces valeurs représentent une avancée par rapport à l'état de l'art qui n'exhibe aucune méthode de traçage robuste à l'enregistrement en salle et valident une première preuve de concept de la méthodologie statistique développée
Sine qua non component of multimedia content distribution on the Internet, video fingerprinting techniques allow the identification of content based on digital signatures(fingerprints) computed from the content itself. The signatures have to be invariant to content transformations like filtering, compression, geometric modifications, and spatial-temporal sub-sampling/cropping. In practice, all these transformations are non-linearly combined by the live camcorder recording use case.The state-of-the-art limitations for video fingerprinting can be identified at three levels: (1) the uniqueness of the fingerprint is solely dealt with by heuristic procedures; (2) the fingerprinting matching is not constructed on a mathematical ground, thus resulting in lack of robustness to live camcorder recording distortions; (3) very few, if any, full scalable mono-modal methods exist.The main contribution of the present thesis is to specify, design, implement and validate a new video fingerprinting method, TrackART, able to overcome these limitations. In order to ensure a unique and mathematical representation of the video content, the fingerprint is represented by a set of wavelet coefficients. In order to grant the fingerprints robustness to the mundane or malicious distortions which appear practical use-cases, the fingerprint matching is based on a repeated Rho test on correlation. In order to make the method efficient in the case of large scale databases, a localization algorithm based on a bag of visual words representation (Sivic and Zisserman, 2003) is employed. An additional synchronization mechanism able to address the time-variants distortions induced by live camcorder recording was also designed.The TrackART method was validated in industrial partnership with professional players in cinematography special effects (Mikros Image) and with the French Cinematography Authority (CST - Commision Supérieure Technique de l'Image et du Son). The reference video database consists of 14 hours of video content. The query dataset consists in 25 hours of replica content obtained by applying nine types of distortions on a third of the reference video content. The performances of the TrackART method have been objectively assessed in the context of live camcorder recording: the probability of false alarm lower than 16 10-6, the probability of missed detection lower than 0.041, precision and recall equal to 0.93. These results represent an advancement compared to the state of the art which does not exhibit any video fingerprinting method robust to live camcorder recording and validate a first proof of concept for the developed statistical methodology
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Books on the topic "Visual fingerprint"

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Tokareva, Elena, Tat'yana Solodova, and Natal'ya Lavrent'eva. Visual Dactyloscopy: in Schemes and Illustrations. ru: INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2188330.

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The educational visual aid contains material for law enforcement personnel of African countries undergoing training and internship at the faculty of retraining and advanced training under the programs «Advanced training for law enforcement personnel of African countries on the topic “Dactyloscopy and Fingerprint Examination”», «Advanced training of law enforcement personnel of African countries on the topic “Modern Expert Diagnostics and Identifi cation. Modern fi ngerprinting and fi ngerprint registration”». The book is illustrated with schemes and pictures that facilitate the perception of lecture material containing a presentation of the scientifi c foundations of dactyloscopy and fi ngerprint examination, as well as private methods of expert research. The publication is designed for teachers and law enforcement personnel of African countries undergoing advanced training at universities of the Ministry of Internal Aff airs of Russia. Translation — Lavrentyeva N.V. (Teacher of the highest category).
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Book chapters on the topic "Visual fingerprint"

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Rahman, S. M. Mahbubur, Tamanna Howlader, and Dimitrios Hatzinakos. "Fingerprint Classification." In Orthogonal Image Moments for Human-Centric Visual Pattern Recognition, 117–28. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9945-0_5.

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Amayeh, Gholamreza, Soheil Amayeh, and Mohammad Taghi Manzuri. "Fingerprint Images Enhancement in Curvelet Domain." In Advances in Visual Computing, 541–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_53.

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Moolla, Yaseen, Ann Singh, Ebrahim Saith, and Sharat Akhoury. "Fingerprint Matching with Optical Coherence Tomography." In Advances in Visual Computing, 237–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27863-6_22.

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Mgaga, Sboniso Sifiso, Jules-Raymond Tapamo, and Nontokozo Portia Khanyile. "Optical Coherence Tomography Latent Fingerprint Image Denoising." In Advances in Visual Computing, 694–705. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64559-5_55.

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Dellys, Hachemi Nabil, Noussaiba Benadjimi, Meriem Romaissa Boubakeur, Layth Sliman, Karima Benatchba, Saliha Artabaz, and Mouloud Koudil. "A Critical Comparison of Fingerprint Fuzzy Vault Techniques." In Advances in Visual Informatics, 178–88. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25939-0_16.

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Xie, Wuyuan, Zhan Song, and Xiaoting Zhang. "A Novel Photometric Method for Real-Time 3D Reconstruction of Fingerprint." In Advances in Visual Computing, 31–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17274-8_4.

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Jiang, Xiang, Shikui Wei, Ruizhen Zhao, Ruoyu Liu, Yufeng Zhao, and Yao Zhao. "A Visual Perspective for User Identification Based on Camera Fingerprint." In Lecture Notes in Computer Science, 52–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34110-7_5.

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Muhammed, Ajnas, and Alwyn Roshan Pais. "A Novel Cancelable Fingerprint Template Generation Mechanism Using Visual Secret Sharing." In Lecture Notes in Computer Science, 357–65. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-12700-7_37.

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Chi, Zhang. "Research on Image Fingerprint Technology Based on Watson Visual Model Multimedia Technology." In Advances in Intelligent Systems and Computing, 127–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60744-3_14.

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Gaudio, Paola. "11. Emotional Fingerprints." In Prismatic Jane Eyre, 546–91. Cambridge, UK: Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0319.17.

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This is a transversal reading of the novel and its Italian translations through the lens of nouns expressing emotions. To this aim, a parallel corpus was compiled, comprising the source text and 11 translations. After identifying the types and range of English emotion-nouns and commenting on their significance in the novel, Italian equivalents are analysed, comparing them against the source text. Such a quantitative study has allowed me to identify patterns and anomalies in how emotion-nouns are used by individual translators. Emotional fingerprints of the source text and of each translation have been created to provide a visual representation of their idiosyncrasies.
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Conference papers on the topic "Visual fingerprint"

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Zhang, Shihao, Zhaodi Pei, Haonan Mou, Wenting Yang, Qing Li, and Xia Wu. "Visual Explanations of Deep Convolutional Neural Network for EEG Brain Fingerprint." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635505.

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Massoudi, A., F. Lefebvre, C. h. Demarty, L. Oisel, and B. Chupeau. "A Video Fingerprint Based on Visual Digest and Local Fingerprints." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312834.

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Li, Haoyue, Ming Fang, and Feiran Fu. "Visual fingerprint-based indoor localization." In the 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3194206.3194237.

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Prange, Sarah, Lukas Mecke, Alice Nguyen, Mohamed Khamis, and Florian Alt. "Don't Use Fingerprint, it's Raining!" In AVI '20: International Conference on Advanced Visual Interfaces. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3399715.3399823.

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Dreher, Andreas W., and Klaus Reiter. "Nerve Fiber Layer Assessment with a Retinal Laser Ellipsometer." In Noninvasive Assessment of the Visual System. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/navs.1991.tua2.

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Retinal ganglion cell axons form the innermost layer of the retina known as the nerve fiber layer. These axons converge to form the optic nerve, which transmits the visual image from the eye to the brain. In glaucoma, there is damage to these nerve fibers as they leave the eye through the lamina cribrosa, a fenestrated network of connective tissue. In the retina, the nerve fibers spread out in a thin layer creating a characteristic fingerprint. It is likely that loss of even a few axons can alter the fingerprint and be observed in the retinal nerve fiber layer when appropriate techniques are used.
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Jain, S., S. K. Mitra, A. Banerjee, and A. K. Roy. "A graphical approach for fingerprint verification." In IET International Conference on Visual Information Engineering (VIE 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20060504.

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Boutella, Leila, and Amina Serir. "Block ridgelet and SVM based fingerprint matching." In 2011 3rd European Workshop on Visual Information Processing (EUVIP). IEEE, 2011. http://dx.doi.org/10.1109/euvip.2011.6045518.

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Mühlbacher, Bernhard, Thomas Stütz, and Andreas Uhl. "JPEG2000 Part 2 wavelet packet subband structures in fingerprint recognition." In Visual Communications and Image Processing 2010, edited by Pascal Frossard, Houqiang Li, Feng Wu, Bernd Girod, Shipeng Li, and Guo Wei. SPIE, 2010. http://dx.doi.org/10.1117/12.862926.

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Hine, Gabriel Emile, Emanuele Maiorana, and Patrizio Campisi. "Minutiae Triple Correlation: A Translation Invariant Fingerprint Representation." In 2019 8th European Workshop on Visual Information Processing (EUVIP). IEEE, 2019. http://dx.doi.org/10.1109/euvip47703.2019.8946139.

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Patil, B. D., J. V. Kulkarni, and R. S. Holambe. "Fingerprint verification using wavelet and local dominant orientation." In IET International Conference on Visual Information Engineering (VIE 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20060506.

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Reports on the topic "Visual fingerprint"

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Stanton, Brian, Mary Theofanos, and Charles Sheppard. A study of users with visual disabilities and a fingerprint process. Gaithersburg, MD: National Institute of Standards and Technology, 2008. http://dx.doi.org/10.6028/nist.ir.7484.

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