Age estimation using active appearance models and support vector machine regression

Authors:
Khoa Luu;Karl Ricanek;Tien D. Bui;Ching Y. Suen
Affiliations:
Centre for Pattern Recognition and Machine Intelligence, Concordia University, Canada;Department of Computer Science, University of North Carolina, Wilmington, North Carolina;Centre for Pattern Recognition and Machine Intelligence, Concordia University, Canada;Centre for Pattern Recognition and Machine Intelligence, Concordia University, Canada
Venue:
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Year:
2009

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Abstract

In this paper, we introduce a novel age estimation technique that combines Active Appearance Models (AAMs) and Support Vector Machines (SVMs), to dramatically improve the accuracy of age estimation over the current state-of-the-art techniques. In this method, characteristics of the input images, face image, are interpreted as feature vectors by AAMs, which are used to discriminate between childhood and adulthood, prior to age estimation. Faces classified as adults are passed to the adult age-determination function and the others are passed to the child age-determination function. Compared to published results, this method yields the highest accuracy recognition rates, both in overall mean-absolute error (MAE) and mean-absolute error for the two periods of human development: childhood and adulthood.