Author: Kuan-Hsien Liu, Shuicheng Yan, and C.-C. Jay Kuo

Facial image processing has attracted a lot of attention in the computer vision community over the last two decades. The human face can reveal important perceptual characteristics such as the identification, gender, race, emotion, pose, age, etc. Among these characteristics, the age information has its particular importance. The aging progress is complicated, nonreversible and uncontrollable. It is affected by various factors, including the living environment, climate, health, life style, and biological reasons. Age-related facial image processing is being extensively studied, and facial age group classification is one of major research topics in this area.

Examples include age-based facial image retrieval, internet access control, security control and surveillance, biometrics, age-based human-computer interaction (HCI), age prediction for finding missing children, and age estimation based on the result of age groups classification. Age estimation can be done more accurately if it is worked on groups containing a narrower age range. Hence, the age group classification problem is an interesting one that demands further efforts.

We presented a structured fusion method for facial age group classification as shown in Figure 1. To utilize the structured fusion of shape features and surface features, we introduced the region of certainty (ROC) to not only control the classification accuracy for shape feature based system but also reduce the classification needs on surface feature based system. In the first stage, we design two shape features, which can be used to classify frontal faces with high accuracies. In the second stage, a surface feature is adopted and then selected by a statistical method. The statistical selected surface features combined with a SVM classifier can offer high classification rates. With properly adjusting the ROC by a single non-sensitive [...]