Unsupervised Facial Pose Grouping via Garbor Subspace Affinity and Self-Tuning Spectral Clustering

Facial pose grouping plays an important role in the video face recognition. In this paper, we present an unsupervised facial pose grouping approach via Garbor subspace affinity and self-tuning spectral clustering. First, we utilize the local normalization method to reduce the impact of uneven illuminations, and then extract the discriminative appearance features via Gabor wavelet representation. Next, the Garbor subspace affinity method is presented to compute an affinity matrix in terms of the pair wise similarity, in which the facial frames of the same pose always share the smaller pair wise similarities. Finally, we employ the self-tuning spectral clustering algorithm to label the affinity matrix, through which the number of pose groups and the corresponding grouping results can be obtained automatically. Without any label priors, the proposed approach is able to well differentiate the distinct facial poses under uneven illuminations, and the experimental results have shown the satisfactory performances.