Facial feature fusion and model selection for age estimation

Automatic face age estimation is challenging due to its complexity owing to genetic difference, behavior and environmental factors, the dynamics of facial aging between different individuals, etc. In this work we propose to fuse the global facial feature extracted from Active Appearance Model (AAM) and the local facial features extracted from Local Binary Pattern (LBP), as the representation of faces. Furthermore, we introduce an advanced age estimation system combining feature fusion and model selection schemes such as Least Angle Regression (LAR) and sequential approaches. Due to the fact that different facial feature representations may come with various types of measurement scales, we compare multiple normalization schemes for both facial features. We demonstrate that the feature fusion with model selection can achieve significant improvement in age estimation over single feature representation alone. Our experiment on multi-ethnicity UIUC-PAL database suggests that age estimation with feature fusion and model selection outperforms the single feature, or the full feature model.

1. Introduction

Human faces contain important information, such as gender, race, mood, and age [2]. Face age estimation has attracted great attentions recently in both research communities and industries, due to its significant role in human computer interaction (HCI), surveillance monitoring, and biometrics. However, there are many intrinsic and extrinsic factors which make it very difficult to predict the ages of human subjects from their face images accurately. The intrinsic factors include genetics, ethnicity, gender, and health conditions. The extrinsic factors include makeup, accessories, facial hair, and the variation of expression, pose and illumination. Furthermore, a face image of size is generally represented by a vector with dimensionality of . It is still a challenging topic to reduce the dimensionality significantly and effectively from the original image space.