The binary classification problem where an input is classified as belonging or not to a certain class, the so-called Target Class (TC), is approached here. This problem can be stated as a basic hypothesis test: X is from the TC ( H 0 ) vs. X is not from the TC ( H 1 ), where X is the classifier input. When probabilistic models are used (e.g., Hidden Markov Models or Gaussian Mixture Models), the likelihood ratio, p ( X / H 0 )/ p ( X / H 1 ), is an alternative widely used to improve the classification. However, as far as we know, this ratio is not usually applied with distance-based classifiers (e.g., Dynamic Time Warping). Following that idea, here we propose making the decision based not only on the score (“score” being the classifier output) assuming X to be from the TC ( H 0 ), but also using the score assuming X is not from the TC ( H 1 ), by means of the ratio between both scores: the score ratio. The proposal is tested in biometric person authentication using manuscript signature, with three different state-of-the-art systems based on distance classifiers. Different alternatives for applying the proposal are shown in order to reduce the computer load, should it prove necessary. Using the score ratio has led to improvements in most of the tests performed. The best verification results were achieved using our proposal, with the best ones without the score ratio being improved by an average of 22%.

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.

This paper presents a system that automatically labels tones and break indices (ToBI) events. The detection (binary classification) of prosodic events has received significantly more attention from researchers than its classification because of the intrinsic difficulty of classification. We focus on the classification problem, identifying eight types of pitch accent tones, nine types of boundary tones and five types of break indices. The complex multi-class classification problem is divided into several simpler problems, by means of pairwise coupling. We propose to combine two-class classifiers to achieve the multi-class classification because two-class problems provide high accuracy results. Furthermore, complementarity between artificial neural networks and decision trees classifiers has been exploited to improve the final system, combining their outputs using a fusion method. This proposal, together with the adequate feature extraction that includes the use of features such as the Tilt and Bézier parameters, allows us to achieve a total classification accuracy of 70.8% for pitch accents, 84.2% for boundary tones and 74.6% for break indices, on the Boston University Radio News Corpus. The analysis of the misclassified samples shows that the types of mistakes that the system makes do not differ significantly from the common confusions that are observed in manual ToBI inter-transcriber tests.

In this study, an application that allows a mobile phone to be used as a biometric-capture device is shown. The main contribution of our proposal is that this capture, and later recognition, can be performed during a standard web session, using the same architecture that is used in a personal computer (PC), thus allowing a multiplatform (PC, personal digital assistant (PDA), mobile phone, etc.) biometric web access. The review, which is from both an academic and commercial point of view, of the biometry and mobile device state of the art shows that in other related works, the biometric capture and recognition is either performed locally in the mobile or remotely but using special communication protocols and/or connection ports with the server. The second main contribution of this study is an in-depth analysis of the present mobile web-browser limitations; thus, it is concluded that, in general, it is impossible to use the same technologies that can be used to capture biometrics in PC platforms (i.e., Applet Java, ActiveX Control, JavaScript, or Flash); therefore, new solutions, as shown here, are needed.

In this paper, we present the main results of the BioSecure Signature Evaluation Campaign (ESRA'2011). The objective of ESRA'2011 is to evaluate through two different tasks the resistance of different online signature systems to skilled forgeries categorized automatically according to their quality. Task 1 aims at studying with only coordinate time functions the influence of acquisition conditions (digitizing tablet vs. PDA) on systems' performance. The two BioSecure Data Sets DS2 and DS3 make this possible, since they contain data from the same 382 people, acquired respectively on a digitizer and on a PDA. Task 2 then aims at assessing the contribution of the five time functions available on a digitizer (coordinates, pressure, pen inclination) on systems' resistance to different qualities of skilled forgeries. Results of the 13 systems involved in this competition are reported and analyzed for both tasks in this paper. We observe that the best system in terms of performance on forgeries of "bad" quality is not necessarily the most resistant to an increased quality of skilled forgeries. Also, we note that mobile conditions are still threatening independently of the quality of forgeries. Finally, when adding pen inclination time functions to pressure and coordinates, we find that the gap between systems in terms of performance is wider than when only pen coordinates and pressure are considered.

This paper deals with the problem of training an Artificial Neural Network (ANN) when the data sets are very imbalanced. Most learning algorithms, including ANN, are designed for well-balanced data and do not work properly on imbalanced ones. Of the approaches proposed for dealing with this problem, we are interested in the re-sampling ones, since they are algorithm-independent. We have recently proposed a new under-sampling technique for the two-class problem, called Non-Target Incremental Learning (NTIL), which has shown a good performance with SVM, improving results and training speed. Here, the advantages of using this technique with ANN are shown. The performance with regard to other popular under-sampling techniques is compared.

We describe an experience of cooperative learning for teaching the UNIX operating system in first year of Computer Engineering that helps the students to work with constancy, motivates them to attend the laboratory sessions, stay with the subject, and promotes social and communication skills. This methodology has been very satisfactorily valued by the students.