Estimation of demographic information from video sequence with people is a topic of growing interest in the last years. Indeed automatic estimation of audience statistics in digital signage as well as the human interaction in social robotic environment needs of increasingly robust algorithm for gender, race and age classification. In the present paper some of the state of the art features descriptors and sub space reduction approaches for gender, race and age group classification in video/image input are analyzed. Moreover a wide discussion about the influence of dataset distribution, balancing and cardinality is shown. The aim of our work is to investigate the best solution for each classification problem both in terms of estimation approach and dataset training. Additionally the computational problem it considered and discussed in order to contextualize the topic in a practical environment.

Carcagni P., Coco M.D., Mazzeo P.L., Testa A., Distante C. (2014). Features descriptors for demographic estimation: A comparative study. HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY : Springer Verlag [10.1007/978-3-319-12811-5_5].

Features descriptors for demographic estimation: A comparative study

Mazzeo P. L.;Testa A.;
2014

Abstract

Estimation of demographic information from video sequence with people is a topic of growing interest in the last years. Indeed automatic estimation of audience statistics in digital signage as well as the human interaction in social robotic environment needs of increasingly robust algorithm for gender, race and age classification. In the present paper some of the state of the art features descriptors and sub space reduction approaches for gender, race and age group classification in video/image input are analyzed. Moreover a wide discussion about the influence of dataset distribution, balancing and cardinality is shown. The aim of our work is to investigate the best solution for each classification problem both in terms of estimation approach and dataset training. Additionally the computational problem it considered and discussed in order to contextualize the topic in a practical environment.
2014
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
66
85
Carcagni P., Coco M.D., Mazzeo P.L., Testa A., Distante C. (2014). Features descriptors for demographic estimation: A comparative study. HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY : Springer Verlag [10.1007/978-3-319-12811-5_5].
Carcagni P.; Coco M.D.; Mazzeo P.L.; Testa A.; Distante C.
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/744011
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 3
social impact