Human Age Estimation and Sex Classification

Part of the Studies in Computational Intelligence book series (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.

Keywords

Face Recognition Support Vector Regression Local Binary Pattern Aging Function Active Appearance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Science and Electrical EngineeringWest Virginia UniversityWest VirginiaUSA

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