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Human Age Estimation and Sex Classification

  • Chapter
Video Analytics for Business Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 409))

Abstract

Collecting demographic information from the customers, such as age and sex, is very important for marketing and customer group analysis. For instance, the marketing study has an interest to know how many people visited a shopping mall, and what is the distribution of the customers, such as how many males and females; how many young, adult, and senior people. Instead of hiring human workers to observe the customers, a computational system might be developed to analyze people who appeared in images and videos captured by cameras installed in a shopping mall, and then gather the demographic information. To develop a real system for age estimation and sex classification, many essential issues have to be addressed. In this chapter, a detailed introduction of the computational approaches to human age estimation and sex classification will be given. Various methods for feature extraction and learning will be described. Major challenges and future research directions will also be discussed. The goal is to inspire new research and encourage deeper investigation towards developing a working system for business intelligence.

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  1. Department of Computer Science and Electrical Engineering, West Virginia University, West Virginia, USA

    Guodong Guo

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  1. Philips Research, High Tech Campus 36, Eindhoven, 5656 AE, Netherlands

    Caifeng Shan

  2. Mitsubishi Electric Research Laboratorie, Broadway 201, Cambridge, 02139, USA

    Fatih Porikli

  3. Dept. Computer Science, Queen Mary University of London, Mile End Rd., London, E1 4NS, United Kingdom

    Tao Xiang

  4. Dept. Computer Science, Queen Mary University of London, Mile End Rd., London, E1 4NS, United Kingdom

    Shaogang Gong

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Guo, G. (2012). Human Age Estimation and Sex Classification. In: Shan, C., Porikli, F., Xiang, T., Gong, S. (eds) Video Analytics for Business Intelligence. Studies in Computational Intelligence, vol 409. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28598-1_4

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