Unsupervised Clustering of Human Pose Using Spectral Embedding

Abstract

Abstract. In this paper we use the spectra of a Hermitian matrix and the coefficient of the symmetric polynomials to cluster different human poses taken by an inexpensive 3D camera, the Microsoft 'Kinect' for XBox 360. We construct a Hermitian matrix from the joints and the angles subtended by each pair of limbs using the three-dimensional 'skeleton' data delivered by Kinect. To compute the angles between a pair of limbs we construct the line graph from the given skeleton. We construct pattern vectors from the eigenvectors of the Hermitian matrix. The pattern vectors are embedded into a pattern-space using Principal Component Analysis (PCA). We compere the results obtained with the Laplacian spectra pattern vectors. The empirical results show that using the angular information can be efficiently used to clusters different human poses.