Automatic online signature verification using HMMs with user-dependent structure

Producción CientíficaA novel strategy for Automatic online Signature Verification based on hidden Markov models (HMM) with user-dependent structure is presented in this work. Under this approach, the number of states and Gaussians giving the optimal prediction results are independently selected for each user. With this simple strategy just three genuine signatures could be used for training, with an EER under 2.5% obtained for the basic set of raw signature parameters provided by the acquisition device. This results increment by a factor of six the accuracy obtained with the typical approach in which claim-independent structure is used for the HMMs.Ministerio de Educación y Formación Profesional (contract TIC2003-08382-C05-03)Junta de Castilla y Leon (project VA053A05

Generación de espacios de representación de firmas dinámicas: una revisión enfocada al análisis de complejidad

Se presenta una revisión de técnicas empleadas para la verificación e identificación biométrica basada en la firma dinámica, además de mostrar estudios realizados respecto a la aplicación del análisis de complejidad en los procesos fisiológicos como la firma. En la revisión se aprecia la necesidad de realizar investigaciones sobre caracterización basada en técnicas de dinámica no lineal, debido a que se desconocen las ventajas del análisis de complejidad en los procesos de identificación biométrica, y las relacionadas con la captura de la dinámica intrínseca del proceso. De manera preliminar se presentan resultados de identificación biométrica con precisión de clasificación de 94.08% usando la base de datos pública SVC

Validation of dynamic signature for identity verification

Machine based identity validation is extremely important to determine the authenticity of documents, for financial transactions, and for e-communication. Recent explosion of frauds have demonstrated the ineffectiveness of password, personal identification numbers and biometrics. This thesis presents a signature verification technique which is inexpensive, user friendly, robust against impostors and is reliable, and insensitive to factors such as users’ exposure to emotional stimuli. This work has addressed three important issues which are: • The selection of appropriate features for dynamic and static signatures. • The suitable classifier for classification of the features. • The impact of emotional stimuli on the natural handwriting and signatures of the subjects. The thesis reports a comparison of the dynamic and static signatures and demonstrates that while the dynamic signature technique has a small increase in the rejection of the authentic user (92% compared with 94%), the system is far more discerning regarding the acceptance of the impostors (1% compared with 21%). The work also demonstrates that the use of ’unknown’ as a class reduces the rejection to zero, by putting those into a class who would be asked to repeat the experiment. This thesis has also studied the impact of emotional stimuli on peoples’ handwriting and signatures and has determined that while the signatures are insensitive to these stimuli, the handwriting is affected by these stimuli. This outcome may be of importance for people who conduct graphology analysis on people because this suggests that while general handwriting is affected by short term emotional changes of people, signatures are a more robust indicator of the person and hence their personality