Linear Canonical Correlation Analysis Based Ranking Approach For Facial Age Estimation

Facial age estimation is an important and challenging problem in computer vision and pattern recognition. Linear canonical correlation analysis (CCA) has been widely applied owing to low complexity, small and fixed amount of model parameters and good scalability. However, linear CCA based regression gets lower accuracy than its kernel version on the age estimation problem. The inexactness of metric distance information carried by age labels increases the complexity of using regression-based methods to estimate age. Hence, we propose a linear 2-norm regularized LS-CCA based ranking approach only exploiting the ordinal information carried by age labels. It gains the advantages of both linear 2-norm regularized LS-CCA and the ranking approach. Our method achieves competitive accuracy with the state-of-the-arts with sharply lower time cost and less amount of model parameters, which makes it appealing for real-time, large-scale applications or embedded systems. Additionally, experimental results on multi-source cross-population age estimation problem demonstrate that it is more robust against race and gender variations than the state-of-the-arts.