Arancha Simon-Hurtado

In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has been followed, using a biometric database composed of data acquired from 38 subjects by means of a commercial smartwatch in two different sessions. A cross-session test (where training and testing data were acquired in different days) was performed. Following the state of the art, the proposal was tested with different configurations in the acquisition, pre-processing, feature extraction and classification stages, achieving improvements in all of the scenarios; improvements of 100% (0% error) were even reached in some cases. This shows the advantages of including the proposed technique, whatever the system.

This article describes the experience and results obtained from teaching computational logic in an introductory course for undergraduates. The proposed teaching approach helps students to reach a deeper level of understanding in first order logic representation, computational logic procedures and automated theorem proving. The article includes the description of SLI, a theorem prover with a graphical output and the results of a survey to assess the acceptance of the tool. © 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:837–845, 2015; View this article online atwileyonlinelibrary.com/journal/cae; DOI10.1002/cae.21654

Score Normalization is a usual technique in pattern recognition to standardize the classifier output ranges so as to, for example, fuse these outputs. The use of score normalization in biometric recognition is a very important part of the system, specially in those based on behavioral traits, such as written signature or voice, conditioning the final system performance. Then, many works can be found that focus on the problem. A successful new approach for client threshold prediction, based on Multiple Linear Prediction, has been presented in recent works. Here, a new approach for score normalization, based on this proposal for biometric manuscript signature user verification, is shown. This proposal is compared with the state of the art methods, achieving an improvement of 19% and 16% for Equal Error Rate (EER) and 60% and 26% for Detection Cost Function (DCF) performance measures, for random and skilled forgeries, respectively.