GETSEL: Gallery Entropy for Template SElection on Large datasets

The ability of a biometric system to reliably recognize individuals, who were registered for authentication as well as security purposes, significantly depends on the kind and amount of variation that the exploited biometric trait may undergo from one acquisition to another. Those variations may be due both to factors related to acquisition devices, e.g. different resolutions, or to different environment settings, e.g., illumination, or to modification of the trait appearance, e.g., pose and expression of the face. One of the straightforward strategies to address issues related to changes in biometric features is to store more templates for the same person, in order to increase the chances to identify her. The problem arises to choose the templates to store in a way which actually achieves better performance, while avoiding flooding the system with an excessively huge gallery. This work proposes an approach for the selection of the best templates for face recognition, which is based on a notion of gallery entropy, and can be also used for large datasets. Though relying on a clustering process, its main achievement is to automatically derive the best number of clusters/prototypes per subject without requiring to fixing it in advance. Comparative tests with existing approaches show that it is a very promising solution

GETSEL: Gallery Entropy for Template SElection on Large datasets

The ability of a biometric system to reliably recognize individuals, who were registered for authentication as well as security purposes, significantly depends on the kind and amount of variation that the exploited biometric trait may undergo from one acquisition to another. Those variations may be due both to factors related to acquisition devices, e.g. different resolutions, or to different environment settings, e.g., illumination, or to modification of the trait appearance, e.g., pose and expression of the face. One of the straightforward strategies to address issues related to changes in biometric features is to store more templates for the same person, in order to increase the chances to identify her. The problem arises to choose the templates to store in a way which actually achieves better performance, while avoiding flooding the system with an excessively huge gallery. This work proposes an approach for the selection of the best templates for face recognition, which is based on a notion of gallery entropy, and can be also used for large datasets. Though relying on a clustering process, its main achievement is to automatically derive the best number of clusters/prototypes per subject without requiring to fixing it in advance. Comparative tests with existing approaches show that it is a very promising solution

Semantically-Informed Deep Neural Networks For Sound Recognition

Deep neural networks (DNNs) for sound recognition learn to categorize a barking sound as a "dog"and a meowing sound as a "cat"but do not exploit information inherent to the semantic relations between classes (e.g., both are animal vocalisations). Cognitive neuroscience research, however, suggests that human listeners automatically exploit higher-level semantic information on the sources besides acoustic information. Inspired by this notion, we introduce here a DNN that learns to recognize sounds and simultaneously learns the semantic relation between the sources (semDNN). Comparison of semDNN with a homologous network trained with categorical labels (catDNN) revealed that semDNN produces semantically more accurate labelling than catDNN in sound recognition tasks and that semDNN-embeddings preserve higherlevel semantic relations between sound sources. Importantly, through a model-based analysis of human dissimilarity ratings of natural sounds, we show that semDNN approximates the behaviour of human listeners better than catDNN and several other DNN and NLP comparison models

Bio-chemical data classification by dissimilarity representation and template selection

The identification and classification of bio-chemical substances are very important tasks in chemical, biological and forensic analysis. In this work we present a new strategy to improve the accuracy of the supervised classification of this type of data obtained from different analytical techniques that combine two processes: first, a dissimilarity representation of data and second, the selection of templates for the refinement of the representative samples in each class set. In order to evaluate the performance of our proposal, a comparative study between three approaches is presented. As a baseline, entropy template selection (ETS) is performed in the original feature space and selected templates are used for training. The underlying concept of the other two alternatives, is the combination of Dissimilarity Representations and ETS. The first alternative performs ETS in the original feature space and uses the selected templates as prototypes for the generation of the dissimilarity space and as training set. The second one represents the data in the dissimilarity space, and next ETS is performed. The experimental results showed that an adequate combination of the representation in the dissimilarity the space and the selection of templates based on entropy, outperformed the baseline in accuracy and/or efficiency for the majority of the problems studied