Online Signature Verification using SVD Method

Online signature verification rests on hypothesis which any writer has similarity among signature samples, with scale variability and small distortion. This is a dynamic method in which users sign and then biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The proposed technique for online signature verification is based on the Singular Value Decomposition (SVD) technique which involves four aspects: I) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. The input device used for this signature verification system is 5DT Data Glove 14 Ultra which is originally design for virtual reality application. The output of the data glove, which captures the dynamic process in the signing action, is the data matrix, A to be processed for feature extraction and matching. This work is divided into two parts. In part I, we investigate the performance of the SVD-based signature verification system using a new matching technique, that is, by calculating the average distance between the different subspaces. In part IJ, we investigate the performance of the signature verification with reducedsensor data glove. To select the 7-most prominent sensors of the data glove, we calculate the F-value for each sensor and choose 7 sensors that gives the highest Fvalue

A manual for clinical training in corrective therapy

Thesis (Ed.M.)--Boston Universit

Off-line handwritten signature recognition by wavelet entropy and neural network

Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%

Discussion of the selection and use of teaching aids and standard materials in the lower school at Perkins School for the Blind

Thesis (Ed.M.)--Boston Universit

Signature verification using grid based feature extraction

Signature does not depend on physical features like that of iris detection, gait, fingerprint, facial features; instead it’s a completely behavioural attribute of an individual. The field of signature verification is broadly classified into two parts i.e. online and offline. Online signature verification deals with signatures obtained from digital tablets or any such device where in addition to spatial features of the signature; time, pressure etc. information is also available. The sole purpose of this research paper is to develop an efficient signature authentication system which is still an important part of biometric identification methods

Addressing the Argument Writing Needs of English Learners in Seventh Grade

The purpose of this curriculum development project was to address the writing needs of English learners (ELs) in the seventh grade English/ Language Arts classroom through modifying the Teachers College Reading and Writing Project’s (TCRWP) Art of Argument Writing Unit with the Sheltered Instruction Observation Protocol (SIOP) model. The curriculum developed by TCRWP required modifications to support ELs in achieving the academic demands of today’s schools. One framework that supports ELs in the classroom is the SIOP model, which is grounded in 15 years of research (Echevarria, Vogt, & Short, 2013). The curriculum developed within this project used the TCRWP Art of Argument Unit as a guide to teaching argumentative writing, but I modified it for EL students using the strategies suggested in the SIOP model

Writing and Flamenco: Phenomenological Investigations

This article asks how the art of music may contribute to the understanding of writing as a phenomenological act. More specifically, I will introduce parts of the vocabulary, theory, and practice of flamenco to investigate the musicality and notably rhythmic qualities of the handwritten, personal signature. The aim is to demonstrate how the introduction of flamenco per se to the qualitative field of writing studies may open a way of thinking of writing as an expressed as well as expressive musical practice. Besides analyzing in detail the flamenco traits of the personal signature the article also scrutinizes the signature-as-music through the phenomenological philosophies of Merleau-Ponty and Derrida. Ultimately the article demonstrates how writing can itself be theorized off the beaten track, following the untraditional clue of guitar play in the flamenco tradition. The portrait that the article paints of writing is thus itself to be considered off the beaten track qua unconventional, unorthodox and, perhaps for some, controversial

Spartan Daily, October 2, 1981

Volume 77, Issue 22https://scholarworks.sjsu.edu/spartandaily/6798/thumbnail.jp

Online Signature Verification using SVD Method

Online signature verification rests on hypothesis which any writer has similarity among signature samples, with scale variability and small distortion. This is a dynamic method in which users sign and then biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The proposed technique for online signature verification is based on the Singular Value Decomposition (SVD) technique which involves four aspects: I) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. The input device used for this signature verification system is 5DT Data Glove 14 Ultra which is originally design for virtual reality application. The output of the data glove, which captures the dynamic process in the signing action, is the data matrix, A to be processed for feature extraction and matching. This work is divided into two parts. In part I, we investigate the performance of the SVD-based signature verification system using a new matching technique, that is, by calculating the average distance between the different subspaces. In part IJ, we investigate the performance of the signature verification with reducedsensor data glove. To select the 7-most prominent sensors of the data glove, we calculate the F-value for each sensor and choose 7 sensors that gives the highest Fvalue