Robust face tracking-by-detection via sparse representation

The robust algorithm, which is used for tracking human faces in unconstrained video, is built on Tracking-by-detection based on sparse representation. The algorithm works by combining the advantages of face tracking and face detection to improve the accuracy of tracking face in complex environment. The off-line trained face model fits input image to detect face and online trained tracker localizes face via sparse representation. Sparse representation makes human faces' tracking more accurate and robust by the generalized Haar-like features. Also, it will make our tracking algorithm more adaptive and robust since it can be used for any original signal, which means K-sparse can be omitted. The algorithm is validated on a surveillance video considering complicated conditions, such as illumination variation, pose changes, and so on.