A framework for joint estimation of age, gender and ethnicity on a large database

• A framework for joint estimation of age, gender and ethnicity in a single step;
• A novel finding on feature dimensionality in estimating age, gender and ethnicity;
• A rank theory based analysis of dimensionality problem in using CCA based methods;
• A ranking of CCA and PLS based methods under our joint estimation framework;
• Investigation of LS formulations of the CCA based methods for our problem.

Human age, gender and ethnicity are valuable demographic characteristics. They are also important soft biometric traits useful for human identification or verification. We present a framework that can estimate the three traits jointly. The joint estimation framework could deal with the mutual influence of age, gender, and ethnicity implicitly. Under this joint estimation framework, we explore different methods for simultaneous estimation of age, gender, and ethnicity. The canonical correlation analysis (CCA) based methods, and partial least squares (PLS) models are explored under our joint estimation framework. Both the linear and nonlinear methods are investigated to measure the performance. We also validate some extensions of these methods, such as the least squares formulations of the CCA methods. We found some consistent ranking of these methods under our joint estimation framework. More importantly, we found that the CCA based methods can derive an extremely low dimensionality in estimating age, gender and ethnicity. An analysis of this property is given based on the rank theory. The experiments are conducted on a very large database containing more than 55,000 face images.