Biometrics

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 [...]

Author: Chun-Ting Huang and C.-C. Jay Kuo

“Facial Recognition” has become an important technique to handle the tremendous growing need for identification and verification since last century. The replacement of traditional transaction by electronic transaction successfully gathered attention for facial recognition from research and business communities, because facial recognition requires no physical interaction on behalf of users. The research on facial recognition can be traced back to early 1990s, from the Eigenface proposed by Turk and Pentland in 1991 [1], which has over 11409 citations on Google Scholar. The follow-up development can be concluded into general directions discussed in Face Recognition Vendor Test – FRVT 2002 [2], and different face databases are developed in order to solve various conditions, such as poses, expressions, and environment. A new database called Long Distance Heterogeneous Face Database (LDHF-DB) [3] is focused on face images under various distances and near-infrared camera, which provides an new challenge within this field.

Since under the long distance, the near-infrared camera can only capture blurred and vague face images, as shown in Fig. 1, causing the template feature’s low performance on LDHF-DB. Therefore, our research only adopts geometric and shape-based features, locally and globally, to determine the input of structured-fusion method. Based on the different characteristics of features we collected from database, we aim to develop an robust classification algorithm with machine learning to distinguish faces under various quality.

The major difference between our work and other research is the feature selection and structured fusion model. I have explained the reason why template method only has a fair performance under the influence of heterogeneous environment. Our proposed model can boost up the recognition rate by adopting different feature’s strength and discarding the outliers for particular [...]