Learning a person-independent representation for precise 3D pose estimation

Precise 3D pose estimation plays a significant role in developing human-computer interfaces and practical face recognition systems. This task is challenging due to the personality in pose variation for a certain subject. In this work, the pose data space is considered as a union of the submanifolds which characterize different subjects, instead of a single continuous manifold as conventionally regarded. A novel manifold embedding algorithm dually supervised by subjects and poses, called Synchronized Submanifold Embedding (SSE), is proposed for person-independent precise pose estimation. First, the submanifold of a certain subject is approximated as a set of simplexes constructed using neighboring samples. Then, these simplexized submanifolds from different subjects are embedded by synchronizing the locally propagated poses within the simplexes and at the same time maximizing the intra-submanifold variances. Finally, the pose of a new datum is estimated as the median of the poses for the nearest neighbors in the dimensionality reduced feature space. The experiments on the 3D pose estimation database, CHIL data for CLEAR07 evaluation demonstrate the effectiveness of our proposed algorithm.