An online spatio-temporal tensor learning model for visual tracking and its applications to facial expression recognition

Robust visual tracking remains a technical challenge in real-world applications, as an object may involve many appearance variations. In existing tracking frameworks, objects in an image are often represented as vector observations, which discounts the 2-D intrinsic structure of the image. By considering an image in its actual form as a matrix, we construct the 3rd order tensor based object representation to preserve the spatial correlation within the 2-D image and fully exploit the useful temporal information. We perform incremental update of the object template using the N-mode SVD to model the appearance variations, which reduces the influence of template drifting and object occlusions. The proposed scheme efficiently learns a low-dimensional tensor representation through adaptively updating the eigenbasis of the tensor. Tensor based Bayesian inference in the particle filter framework is then utilized to realize tracking. We present the validation of the proposed tracking system by conducting the real-time facial expression recognition with video data and a live camera. Experiment evaluation on challenging benchmark image sequences undergoing appearance variations demonstrates the significance and effectiveness of the proposed algorithm.