Abstract

Deep Learning has becoming a popular and effective way to address a large set of issues. In particular, in computer vision, it has been exploited to get satisfying recognition performance in unconstrained conditions. However, this wild race towards even better performance in extreme conditions has overshadowed an important step i.e. the assessment of the impact of this new methodology on traditional issues on which for years the researchers had worked. This is particularly true for biometrics applications where the evaluation of deep learning has been made directly on newest large and more challencing datasets. This lead to a pure data driven evaluation that makes difficult to analyze the relationships between network configurations, learning process and experienced outcomes. This paper tries to partially fill this gap by applying a DNN for gender recognition on the MORPH dataset and evaluating how a lower cardinality of examples used for learning can bias the recognition performance.

Autore Pugliese

• Leo Marco , Mazzeo Pier Luigi

Tutti gli autori

• M. D. Coco ; P. Carcagnì ; M. Leo ; P.L. Mazzeo ; P. Spagnolo

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2016

ISSN

Non Disponibile

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile