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A signature verification algorithm based on static and dynamic fea- tures of online signature data is presented. Texture and topological features are the static features of a signature image whereas the digital tablet captures in real-time the pressure values, breakpoints, and the time taken to create a signa- ture. 1D - log Gabor wavelet and Euler...
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Context 1
... block diagram of the signature verification system is shown in Figure 1. Forgery of signatures can be classified as: a) Random forgery, where the forger randomly guesses the signature, b) Skilled forgery, where the forger has prior knowledge of the signature and might have practiced in advance, and c) Tracing, where a signature instance is used as a reference to attempt forgery. ...
Context 2
... data acquisition process involves reading the reference signature data with the help of a digitizing tablet and obtaining the dynamic parameters (pressure, break- points and total time for a signature) and the image of the signature (Figure 1). Next, the input data is preprocessed using a low-pass filter to eliminate spurious noise in- herent in the acquisition process [2]. ...
Citations
... It is a critical property of images that remains unaffected by a variety of image transformations, including translations, rotations, scaling, projections, and even non-linear deformations. It has traditionally been utilized in a wide range of applications, including signature verification [49] and malaria parasite detection in blood pictures [50]. ...
Adaptive lighting systems can be designed to detect the spatial characteristics of the visual environment and adjust the light output to increase visual comfort and performance. Such systems would require computational metrics to estimate occupants’ visual perception of indoor environments. This paper describes an experimental study to investigate the relationship between the perceived quality of indoor environments, personality, and computational image quality metrics. Forty participants evaluated the visual preference, clarity, complexity, and colorfulness of 50 images of indoor environments. Twelve image quality metrics (maximum local variation (MLV), spatial frequency slope (α), BRISQUE, entropy (S), ITU spatial information (SI), visual complexity (Rspt), colorfulness (M), root mean square (RMS) contrast, Euler, energy (E), contour, and fractal dimension) were used to estimate participants’ subjective evaluations. While visual clarity, visual complexity, and colorfulness could be estimated using at least one metric, none of the metrics could estimate visual preference. The results indicate that perceived colorfulness is highly correlated with perceived clarity and complexity. Personality traits tested by the 10-item personality inventory (TIPI) did not impact the subjective evaluations of the indoor environmental images. Future studies will explore the impact of target and background luminance on the perceived quality of indoor images.
... dynamic signature [100], [101] provides higher accuracy compared to that obtained from static signature, this is due to exploiting temporal data that enhance the decision process, subsequently, the overall system becomes more robust to presentation attack [102]; -a skilled forger considers all previous comments to identify the target sensor and exploit the vulnerability. ...
... Motion magnification [129] Handwritten signature Dynamic analysis [14], [15], [100], [130], [131] Table 8 shows the different methods which have been investigated in the literature for the different modalities. ...
... These types of topological properties remain invariant under any arbitrary rubber-sheet transformation, i.e., stretching, shrinking, rotation etc. and thus is very useful in image characterization to match shapes, recognize objects, image database retrieval and other image processing, and computer vision applications. Analysis of images of real systems like soil crack patterns [1,4], fast reading of car number plates [5], and automatic signature matching [6] have been facilitated through use of Euler numbers. In diagnostic imaging, analysis of patterns with proper thresholding, is extremely important to identify irregularities indicating possible medical conditions. ...
We report some novel properties of a square lattice filled with white sites, randomly occupied by black sites (with probability p). We consider connections up to second nearest neighbors, according to the following rule. Edge-sharing sites, i.e. nearest neighbors of similar type are always considered to belong to the same cluster. A pair of black corner-sharing sites, i.e. second nearest neighbors may form a 'cross-connection' with a pair of white corner-sharing sites. In this case assigning connected status to both pairs simultaneously, makes the system quasi-three dimensional, with intertwined black and white clusters. The two-dimensional character of the system is preserved by considering the black diagonal pair to be connected with a probability q, in which case the crossing white pair of sites are deemed disjoint. If the black pair is disjoint, the white pair is considered connected. In this scenario we investigate (i) the variation of the Euler number versus p graph for varying q, (ii) variation of the site percolation threshold with q and (iii) size distribution of the black clusters for varying p, when q=0.5. We also discuss the earlier proposed 'Island-Mainland' transition (Khatun, T., Dutta, T. & Tarafdar, S. Eur. Phys. J. B (2017) 90: 213) and show mathematically that the proposed transition is not in fact a critical phase transition and does not survive finite size scaling. It is also explained mathematically why clusters of size 1 are always the most numerous.
... These type of topological properties remain invariant under any arbitrary rubber-sheet transformation, i.e. stretching, shrinking, rotation etc. and thus are very useful in image characterization to match shapes, recognize objects, image database retrieval and other image processing and computer vision applications. Analysis of images of real systems like soil crack patterns [3,4], fast reading of car number plates [5] and automatic signature matching [6] have been facilitated through use of Euler numbers. In diagnostic imaging, analysis of patterns with proper thresholding, is extremely important to identify irregularities indicating possible medical conditions. ...
We report some novel properties of a square lattice filled with white sites, randomly occupied by black sites (with probability p). We consider connections up to second nearest neighbours, according to the following rule. Edge-sharing sites, i.e. nearest neighbours of similar type are always considered to belong to the same cluster. A pair of black corner-sharing sites, i.e. second nearest neighbours may form a 'cross-connection' with a pair of white corner-sharing sites. In this case assigning connected status to both pairs simultaneously, makes the system quasi-three dimensional, with intertwined black and white clusters. The two-dimensional character of the system is preserved by considering the black diagonal pair to be connected with a probability q, in which case the crossing white pair of sites are deemed disjoint. If the black pair is disjoint, the white pair is considered connected. In this scenario we investigate (i) the variation of the Euler number χ(p) [= N B (p) − N W (p)] versus p graph for varying q, (ii) variation of the site percolation threshold with q and (iii) size distribution of the black clusters for varying p, when q = 0.5. Here N B is the number of black clusters and N W is the number of white clusters, at a certain probability p. We also discuss the earlier proposed 'Island-Mainland' transition (Khatun, T., Dutta, T. & Tarafdar, S. Eur. Phys. J. B (2017) 90: 213) and show mathematically that the proposed transition is not in fact a critical phase transition and does not survive finite size scaling. It is also explained mathematically why clusters of size 1 are always the most numerous.
... Entre los descriptores topológicos se encuentra el número de Euler que ha sido utilizada en varias aplicaciones de visión por computadora [77], [78]. Esta característica relaciona el número de componentes del objeto y el número de hoyos de la siguiente manera: Calcular el número de Euler a partir de la matriz de adyacencia cuando la imagen presenta objetos muy grandes puede ser ineficiente debido a que ocurre desbordamiento de pila por las llamadas recursivas en la función de recorrido. ...
For more than two decades, the recognition based on the texture of the iris has been studied
in various areas, social and industrial and had been developed biometric systems of
recognition of people. The iris is one of the most reliable biometric features for the
recognition of people. This is mainly due to its stability, its invariance in time and of course
to the uniqueness of the texture patterns that are in the irises of the people. However, there
are stages within the iris based recognition system that still require greater robustness, since
one of the current challenges worldwide is the recognition of people at important distances.
The feature extraction stage represents a challenge for recognition at distance and is an open
field of research due to its being affected in terms of inaccurate occlusions, deformations and
locations. In this work an analysis of the intrinsic characteristics of texture of iris and methods
for represent texture of the iris was made. A new model of representation of the information
of the iris was developed, that allows a robustness in the recognition of people at a distance
under different conditions. Based on the analysis of the related bibliography, the iris texture
modeling approach is established, which takes into account variations in intensity and
contrast in the iris before changes in illumination, as well as the obtaining of distinctive
regions of the iris. The evaluation of the methods of extraction of characteristics leads to the
selection of the method with greater performance in light changes. Regarding the extraction
of regions, a method that takes into account geometric approximations is used. The
integration of the methods generated the proposed model of this thesis work which presents
a good performance in the recognition of people under uncontrolled conditions. The
performance of the obtained model is compared with representations of single criterion and
is evaluated with iris images acquired under different conditions.
... There are many such factors that influence the signature of a person, but none of them affects as much as the time duration itself. If a signature is used as a verification technique, then there may be requirements of using the same signature that was taken first up, to be used as a reference signature [13][14][15][16]. If an automated verification technique is to be implemented for signature verification than the time factor must be given a superior importance. ...
Signature verification is one of the most widely accepted verification methods in use. The application of handwritten signatures includes the banker's checks, the credit and debit cards issued by banks and various legal documents. The time factor plays an important role in the framing of signature of an individual person. Signatures can be classified as: offline signature verification and online signature verification. In this paper a time independent signature verification using normalized weighted coefficients is presented. If the signature defining parameters are updated regularly according to the weighted coefficients, then the performance of the system can be increased to a significant level. Results show that by taking normalized weighted coefficients the performance parameters, FAR and FRR can be improved significantly. Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved.
... There are many such factors that influence the signature of a person, but none of them affects as much as the time duration itself. If a signature is used as a verification technique, then there may be requirements of using the same signature that was taken first up, to be used as a reference signature [13][14][15][16]. If an automated verification technique is to be implemented for signature verification than the time factor must be given a superior importance. ...
p>Signature verification is one of the most widely accepted verification methods in use. The application of handwritten signatures includes the banker’s checks, the credit and debit cards issued by banks and various legal documents. The time factor plays an important role in the framing of signature of an individual person. Signatures can be classified as: offline signature verification and online signature verification. In this paper a time independent signature verification using normalized weighted coefficients is presented. If the signature defining parameters are updated regularly according to the weighted coefficients, then the performance of the system can be increased to a significant level. Results show that by taking normalized weighted coefficients the performance parameters, FAR and FRR, can be improved significantly.</p
... The fusion of static and dynamic signature to enhance the performance of automatic recognition systems has already been studied in several works, where it has been shown that such a fusion approach can yield a significant decrease in the error rates [8,[16][17][18]. Although all of them represent very valuable research efforts, in most of these previous approaches, experiments are carried out on small proprietary databases which do not contain real off-line data (static signatures are generated as single stroke images from the on-line version) or where on-line and off-line samples were not acquired simultaneously but on different sessions. ...
... The first work that effectively studied the potential fusion between on-line and off-line signature verification systems was reported in [16]. The authors used a proprietary database captured with a digitizing tablet to analyse the performance of (i) an on-line verifier based on the pressure, pen-ups and total duration of the signature; and (ii) an off-line authentication system based on a feature vector extracted applying 1D-Log Gabor wavelets and Euler numbers. ...
... Using behavioral biometric features is also a possible way to authenticate a user. For example, Vatsa et al. proposed a signature verification algorithm based on static and dynamic features in[13]. Dynamic features, such as pressure and time, are captured as part of the signature, and compared to stored measurements to determine a matching score. ...
... Afterwards genetic algorithm was used for feature selection and finally neural network was applied to train the system. In 2004, Vatsa et al. [7], proposed a framework signature verification framework (Vatsa et al., 2004) using static and dynamic features of the signature image. In this work, they proposed an online signature verification method that used online and offline features. ...
Recent growth of technology has also increased identification insecurity. Signature is a unique feature which is different for every other person, and each person can be identified using their own handwritten signature. Gender identification is one of key feature in case of human identification. In this paper, a feature based gender detection method has been proposed. The proposed framework takes handwritten signature as an input. Afterwards, several features are extracted from those images. The extracted features and their values are stored as data, which is further classified using Back Propagation Neural Network (BPNN). Gender classification is done using BPNN which is one of the most popular classifier. The proposed system is broken into two parts. In the first part, several features such as roundness, skewness, kurtosis, mean, standard deviation, area, Euler number, distribution density of black pixel, entropy, equi-diameter, connected component (cc) and perimeter were taken as feature. Then obtained features are divided into two categories. In the first category experimental feature set contains Euler number, whereas in the second category the obtained feature set excludes the same. BPNN is used to classify both types of feature sets to recognize the gender. Our study reports an improvement of 4.7% in gender classification system by the inclusion of Euler number as a feature.
