Abstract
Generalized additive models are well-known as a powerful and palatable predictive modelling technique. Scorecards, the discretized version of generalized additive models, are a long-established method in the industry, due to its balance between simplicity and performance. Scorecards are easy to apply and easy to understand. Moreover, in spite of their simplicity, scorecards can model nonlinear relationships between the inputs and the value to be predicted. In the scientific community, scorecards have been largely overlooked in favor of more recent models such as neural networks or support vector machines. In this paper, we address scorecard development, introducing a new formulation more suitable to support regularization. We tackle both the binary and the ordinal data classification problems. In both settings, the proposed methodology shows advantages when evaluated using real datasets.

Abstract
Reject option is a technique used to improve classifier's reliability in decision support systems. It consists on withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue have been concerned with implementing a reject option by endowing a supervised learning scheme (e.g., Multilayer Perceptron, Learning Vector Quantization or Support Vector Machines) with a reject mechanism. In this paper we introduce variants of the Self-Organizing Map (SOM), originally an unsupervised learning scheme, to act as supervised classifiers with reject option, and compare their performances with that of the MLP classifier. © 2014 Springer International Publishing Switzerland.

Abstract
In recent years, periocular recognition has become a popular alternative to face and iris recognition in less ideal acquisition scenarios. An interesting example of such scenarios is the usage of mobile devices for recognition purposes. With the growing popularity and easy access to such devices, the development of robust biometric recognition algorithms to work under such conditions finds strong motivation. In the present work we assess the performance of extended versions of two state-of-the-art periocular recognition algorithms on the publicly available CSIP database, a recent dataset composed of images acquired under highly unconstrained and multi-sensor mobile scenarios. The achieved results show each algorithm is better fit to tackle different scenarios and applications of the biometric recognition problem.

Abstract
Deep Transfer Learning (DTL) emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. Even though DTL offers a greater flexibility in extracting high-level features and enabling feature transference from a source to a target task, the DTL solution might get stuck at local minima leading to performance degradation-negative transference-, similar to what happens in the classical machine learning approach. In this paper, we propose the Source-Target-Source (STS) methodology to reduce the impact of negative transference, by iteratively switching between source and target tasks in the training process. The results show the effectiveness of such approach.

Abstract

Abstract
Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.