Ordinal Hyperplanes Ranker with Cost Sensitivities for Age Estimation

In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which estimates human ages via facial images. The design of the algorithm is based on the relative order information among the age labels in a database. Each ordinal hyperplane separates all the facial images into two groups according to the relative order, and a cost-sensitive property is exploited to find better hyperplanes based on the classification costs. Human ages are inferred by aggregating a set of preferences from the ordinal hyperplanes with their cost sensitivities. Our experimental results demonstrate that the proposed approach outperforms conventional multiclass-based and regressionbased approaches as well as recently developed rankingbased age estimation approaches.

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Kuang-Yu Chang, Chu-Song Chen, Yi-Ping Hung
• Keywords: Computer Vision, CVPR 2011, Facial Images, Ordinal Hyperplanes, Relative Order