Regression from patch-kernel

In this paper, we present a patch-based regression framework for addressing the human age and head pose estimation problems. Firstly, each image is encoded as an ensemble of orderless coordinate patches, the global distribution of which is described by Gaussian Mixture Models (GMM), and then each image is further expressed as a specific distribution model by Maximum a Posteriori adaptation from the global GMM. Then the patch-kernel is designed for characterizing the Kullback-Leibler divergence between the derived models for any two images, and its discriminating power is further enhanced by a weak learning process, called inter-modality similarity synchronization. Finally, kernel regression is employed for ultimate human age or head pose estimation. These three stages are complementary to each other, and jointly minimize the regression error. The effectiveness of this regression framework is validated by three experiments: 1) on the YAMAHA aging database, our solution brings a more th...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Shuicheng Yan, Xi Zhou, Ming Liu, Mark Hasegawa-Johnson, Thomas S. Huang
• Keywords: Age Estimation Error, Computer Vision, CVPR 2008, Head Pose Database, Head Pose Estimation, Patch-based Regression Framework, Regression Error