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Features Descriptors for Demographic Estimation: A Comparative Study

  • Conference paper
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Video Analytics for Audience Measurement (VAAM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8811))

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.

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Notes

  1. 1.

    www.bytefish.de/blog/local_binary_patterns/

  2. 2.

    www.vlfeat.org

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Authors and Affiliations

  1. CNR-INO, Arnesano, Italy

    Pierluigi Carcagnì, Marco Del Coco, Pier Luigi Mazzeo, Andrea Testa & Cosimo Distante

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  1. Pierluigi Carcagnì
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  2. Marco Del Coco
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  3. Pier Luigi Mazzeo
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  4. Andrea Testa
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  5. Cosimo Distante
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Correspondence to Marco Del Coco .

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Editors and Affiliations

  1. National Research Council of Italy, Arnesano, Lecce, Italy

    Cosimo Distante

  2. Dipartimento di Matematica e Informatica, University of Catania IT, Catania, Catania, Italy

    Sebastiano Battiato

  3. Queen Mary University of London, London, United Kingdom

    Andrea Cavallaro

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Carcagnì, P., Del Coco, M., Mazzeo, P.L., Testa, A., Distante, C. (2014). Features Descriptors for Demographic Estimation: A Comparative Study. In: Distante, C., Battiato, S., Cavallaro, A. (eds) Video Analytics for Audience Measurement. VAAM 2014. Lecture Notes in Computer Science(), vol 8811. Springer, Cham. https://doi.org/10.1007/978-3-319-12811-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-12811-5_5

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