Pose-Dependent Low-Rank Embedding for Head Pose Estimation

Abstract

Head pose estimation via embedding model has beendemonstrated its effectiveness from the recent works.However, most of the previous methods only focuson manifold relationship among poses, while overlookthe underlying global structure among subjects and poses.To build a robust and effective head pose estimator,we propose a novel Pose-dependent Low-Rank Embedding(PLRE) method, which is designed to exploita discriminative subspace to keep within-pose samplesclose while between-pose samples far away. Specifically,low-rank embedding is employed under the multitaskframework, where each subject can be naturallyconsidered as one task. Then, two novel terms are incorporatedto align multiple tasks to pursue a better posedependentembedding. One is the cross-task alignmentterm, aiming to constrain each low-rank coefficient toshare the similar structure. The other is pose-dependentgraph regularizer, which is developed to capture manifoldstructure of same pose cross different subjects. Experimentson databases CMU-PIE, MIT-CBCL, and extendedYaleB with different levels of random noise areconducted and six embedding model based baselinesare compared. The consistent superior results demonstratethe effectiveness of our proposed method.