A Novel Hybrid Feature Framework for Multi-View Age Estimation

Facial age estimation has grasped the attention of numerous researchers in recent times. It is a challenging task as a consequence of illumination, pose variations, occlusion, complex background, facial expression, and facial makeup. Estimating the age of an individual with an arbitrary pose is quite a challenging job because most of the age estimation system focuses on the frontal view. In this paper, a novel framework for multi-view age estimation by amalgamating the local and global features is proposed. A novel texture feature, Median Gradient Ternary Pattern is proposed in this paper. The Pseudo Zernike Moment extracts the shape features and the View-based Active Appearance Model constructs an appearance model from the facial images. Further, all three features are combined into a feature vector by executing feature-level fusion. The dimension of the combined feature is reduced using Principal Component Analysis. Multi-class Support Vector Machine is utilized to divide the images into four poses. For each pose, a Support Vector Regression with RBF kernel is applied to train a model for estimating the actual age of an individual. The proposed methodology is performed on two databases, namely, FG-NET and CACD which showcase eminent performance.