While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattern Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods and some well-established classification methods. Moreover, a comparison with human perception ability on age estimation is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers. A number of exciting applications of this technology will be discussed.

This presentation is based on the paper Geng, X., Zhou, Z.-H., and Smith-Miles, K. A., "Automatic Age Estimation Based on Facial Aging Patterns", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 29, no. 12, pp. 2234-2240, 2007.

Biography: Kate Smith-Miles is currently Professor and Head of the School of Engineering and Information Technology at Deakin University. Prior to joining Deakin University in 2006, she was a Professor in the Faculty of Information Technology at Monash University, Australia, where she held a variety of leadership roles over the previous ten years including Deputy Head and Director of Research for the School of Business Systems, and co-Director of the Monash Data Mining Centre. She obtained a first-class B.Sc(Hons) in Mathematics from the University of Melbourne, with a thesis titled "The Destruction of KAM Tori in Chaotic Dynamical Systems", and a Ph.D. from the University of Melbourne, in conjunction with CSIRO Division of Mathematics and Statistics, on the topic "Solving Combinatorial Optimisation Problems Using Neural Networks". Kate has published 2 books and over 175 refereed journal and international conference papers in the areas of combinatorial optimization, neural networks, intelligent systems and data mining. She has supervised to completion 16 PhD students, and has been awarded over AUD$1.5 million in competitive grants, including 7 Australian Research Council grants and industry awards. She is on the editorial board of several international journals, and has been a member of the organizing committee for over 50 international conferences, including several as chair. She is a frequent reviewer of international research activities including grant applications in Canada, U.K., Finland, Hong Kong, Singapore and Australia, refereeing for international research journals, and PhD examinations. In addition to her academic activities, she has also conducted numerous consulting projects with industry, four of which have turned into ARC Linkage grants.

Convenor: Ian Wanless