Automatic Age Estimation Based on Facial Aging Patterns

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 12; pp. 2234 - 2240
• Main Authors: XIN GENG, ZHOU, Zhi-Hua, SMITH-MILES, Kate
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.12.2007; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Age; age estimation; Aging; Aging - physiology; Algorithms; Anthropometry - methods; Applied sciences; Artificial Intelligence; Atomic absorption analysis; Biometry - methods; Computer science; control theory; systems; Computer Simulation; Computer vision; Data mining; Exact sciences and technology; Face - anatomy & histology; Face - physiology; face and gesture recognition; Face detection; Face recognition; Facial; Human; Humans; Image databases; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image reconstruction; machine learning; Mathematical models; Models, Anatomic; Models, Biological; Pattern recognition; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Reproducibility of Results; Senior members; Sensitivity and Specificity; Spectroscopy; Studies; Subspaces; Support vector machines; Computer vision; machine learning; age estimation; pattern recognition; face and gesture recognition; Face recognition; Image processing; Gesture recognition; Sex; Pattern recognition; Modeling; Image reconstruction; Vector space; Classification; Facies; Observer; Pattern analysis; Artificial intelligence; Age
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2007.70733

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 (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age 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.