BioSecure Signature Evaluation Campaign (ESRA'2011): evaluating systems on quality-based categories of skilled forgeries

A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

“…Hence, they will form a cluster located close to the origin in DS. The quality of a forgery can be measured by its proximity to a target signature (Houmani et al, 2011); this proximity should be considered in the feature space. When transposed to the DS, it is expected that, while bad quality skilled forgeries generate negative samples more distant to the origin, good quality skilled forgeries generate samples closer to the origin, and may even be within the positive cluster.…”

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

“…Diaz et al [2] presented a recent update on automatic signature verification (ASV). The research community made significant efforts for acquiring several online signature corpora [1][2][3][4][5][6][7][8][9][10][11] and conducting international evaluations of ASV systems [2,6,[12][13][14][15][16].…”

“…The relative improvement for all user categories is of at least 93% compared to the usual signature.Sensors 2020, 20, 933 2 of 21 sensor technology, interoperability, setting several new challenging issues that impact verification performance [2,17].Usually, for improving verification performance, different strategies were exploited in the literature: (i) acquiring signatures in controlled conditions [1-16]; (ii) using a high quality sensor (such as a Wacom tablet) with high temporal and spatial resolution, and able to capture other time functions than pen coordinates, as pen pressure and pen inclination angles [18]; (iii) selecting reference signatures in order to control intra-personal variability [19][20][21]; (iv) extracting several features for signature description (as pressure, speed, and acceleration, etc.) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] or by means of a deep neural network [2,[22][23][24][25].However, some of these strategies are no longer possible in the mobile scenario: as pointed out by [17], the sensors are not of the same quality, in terms of temporal resolution in particular, acquisition conditions are highly variable, and some sensors are limited to the capture of only pen coordinates. In the so-called "cloud scenario" [17], users acquire their signatures as they want, standing, sitting or moving, handling the device on the hand at different angles or orientations, or placing it on any support.…”

“…A smartphone is usually handheld, while a tablet may be placed on the desktop or sustained by the left arm if the writer is right-handed. The consequence is that verification performance is strongly degraded in mobile conditions [2,15,16,[26][27][28][29][30][31][32][33][34][35][36][37][38][39].In the present paper, we study the online signature biometrics in the framework of uncontrolled mobile conditions. The challenging question then is how to improve verification performance in uncontrolled mobile conditions?…”

“…Usually, for improving verification performance, different strategies were exploited in the literature: (i) acquiring signatures in controlled conditions [1-16]; (ii) using a high quality sensor (such as a Wacom tablet) with high temporal and spatial resolution, and able to capture other time functions than pen coordinates, as pen pressure and pen inclination angles [18]; (iii) selecting reference signatures in order to control intra-personal variability [19][20][21]; (iv) extracting several features for signature description (as pressure, speed, and acceleration, etc.) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] or by means of a deep neural network [2,[22][23][24][25].…”