Comparative Study of Human Age Estimation with or without Preclassification of Gender and Facial Expression

Age estimation has many useful applications, such as age-based face classification, finding lost children, surveillance monitoring, and face recognition invariant to age progression. Among many factors affecting age estimation accuracy, gender and facial expression can have negative effects. In our research, the effects of gender and facial expression on age estimation using support vector regression (SVR) method are investigated. Our research is novel in the following four ways. First, the accuracies of age estimation using a single-level local binary pattern (LBP) and a multilevel LBP (MLBP) are compared, and MLBP shows better performance as an extractor of texture features globally. Second, we compare the accuracies of age estimation using global features extracted by MLBP, local features extracted by Gabor filtering, and the combination of the two methods. Results show that the third approach is the most accurate. Third, the accuracies of age estimation with and without preclassification of facial expression are compared and analyzed. Fourth, those with and without preclassification of gender are compared and analyzed. The experimental results show the effectiveness of gender preclassification in age estimation

Pose-Invariant 3D Face Alignment

Face alignment aims to estimate the locations of a set of landmarks for a given image. This problem has received much attention as evidenced by the recent advancement in both the methodology and performance. However, most of the existing works neither explicitly handle face images with arbitrary poses, nor perform large-scale experiments on non-frontal and profile face images. In order to address these limitations, this paper proposes a novel face alignment algorithm that estimates both 2D and 3D landmarks and their 2D visibilities for a face image with an arbitrary pose. By integrating a 3D deformable model, a cascaded coupled-regressor approach is designed to estimate both the camera projection matrix and the 3D landmarks. Furthermore, the 3D model also allows us to automatically estimate the 2D landmark visibilities via surface normals. We gather a substantially larger collection of all-pose face images to evaluate our algorithm and demonstrate superior performances than the state-of-the-art methods