Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization

Human facial age estimation has attracted much attention due to its potential applications in forensics, security, and biometrics. In contrast to existing approaches that cast facial age estimation as either a multiclass classification or regression problem, in this work, we propose a novel approach that combines the strength of cost-sensitive label ranking methods with the power of low-rank matrix recovery theories. Instead of having to make a binary decision for each age label, our approach ranks age labels in a descending order in terms of their predicted relevance to the given facial image. In addition, the proposed approach aggregates the linear prediction functions for different ages into a matrix, and introduces the matrix trace norm regularization to explicitly capture the correlations among different age labels and control the model complexity as well. Furthermore, motivated by nonlinear generalization performance of kernel methods, we extend the trace norm regularization from a finite dimensional space to an infinite dimensional space. We also provide theoretical analysis on the efficiency of the proposed kernelized trace normalization, which guarantees the feasibility of the proposed method for solving large-scale prediction problems. Comprehensive experiments on multiple well-known facial image datasets demonstrate the effectiveness of the proposed framework for age estimation compared to the state-of-the-arts.