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Facial Analysis With a Lie Group Kernel

To efficiently deal with the complex nonlinear variations of face images, a novel Lie group (LG) kernel is proposed in this paper to address the facial analysis problems. First, we present a linear dynamic model (LDM)-based face representation to capture both the appearance and spatial information of the face image. Second, the derived LDM can be parameterized as a specially structured upper triangular matrix, the space of which is proved to constitute an LG. An LG kernel is then designed to characterize the similarity between the LDMs for any two face images and the kernel can be fed into classical kernel-based classifiers for different types of facial analysis. Finally, experimental evaluations on face recognition and head pose estimation are conducted on several challenging data sets and the results show that the proposed algorithm outperforms other facial analysis methods.