New PDF release: Fundamentals of Sketch-Based Passwords: A General Framework

By Benjamin S. Riggan, Wesley E. Snyder, Cliff Wang

ISBN-10: 3319136283

ISBN-13: 9783319136288

ISBN-10: 3319136291

ISBN-13: 9783319136295

This SpringerBrief explores graphical password platforms and examines novel drawing-based tools by way of safety, usability, and human computer-interactions. It offers a scientific method for spotting, evaluating, and matching sketch-based passwords within the context of contemporary computing structures. The publication bargains either a safety and usefulness research of the accumulative framework used for incorporating handwriting biometrics and a human computer-interaction functionality research. The chapters supply new views and experimental effects relating to version forte, reputation tolerance, and the human-computer interplay. the consequences reveal that biometrics lessen the equivalent blunders cost (EER) by means of greater than 10%, and convey that folks are able to properly reproducing a sketch-based password. basics of Sketch-based Passwords: A basic Framework objectives computing device scientists and engineers excited about computing device safeguard, biometrics, and human components. Advanced-level scholars in computing device technology and electric engineering will locate this fabric valuable as a examine advisor for his or her classes.

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Extra info for Fundamentals of Sketch-Based Passwords: A General Framework

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Equivalently, let the features from the collection of sketches be denoted as {vτ }cτ =1 , and then the representative model may be defined as: nc exp (v − vτ )T Σ −1 (v − vτ ) m(v) ¯ = τ =1 which is very similar to Eq. 9, except for the number of elements being summed. e. a sketch-based password for particular user), then the model estimate, m(v), ¯ is improved from the estimate obtain using a single 28 3 Sketch-Based Authentication sketch (with n sample points). Knowing that a kernel density estimation, p(v) ˆ converges to the “true” probability density p(v) as the n → ∞ (provided that the bandwidth inversely scales with n), a similar argument can be made in regard to m(v) ¯ because of the relationship between the SKS model and kernel density estimation.

DTW uses dynamic programming to find the minimum dissimilarity between sketches. 3. SKS attempts to determine the maximum similarity between sketches. 4. SKS is similar a multivariate kernel density estimator, except that the SKS model does not integrate to unity. 5. The SKS framework extended to an arbitrary number of sketch properties, including biometrics. 6. There are two ways to compare a test sketch with multiple sketches: (1) determine similarity of a test sketch with all representatives, or constructed an “average” representation from exemplars (and make only one comparison).

Therefore, the feature space is a 5D, where the respective dimension are: distance (ρ), curvature (κ), direction (θ ), order (s), and pressure (b). Next, the database that used for the HCI analysis is briefly discussed. 2 45 BioSketch Database The ground truth problem is common among HCI applications, and is usually handled through controlled studies. Therefore, the BioSketch database [5], which was constructed in controlled environment with limited (if any) biasing effects, is used to provide a controlled variability analysis.

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Fundamentals of Sketch-Based Passwords: A General Framework by Benjamin S. Riggan, Wesley E. Snyder, Cliff Wang


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