A New Method for Multi-view Face Clustering in Video Sequence

Abstract

In the problem of face clustering with multi-views, the similarity between faces of different persons with similar pose is usually greater than the similarity between multi-view faces of the same person. This may exert a tremendous impact on the clustering result that sent back to the user. To solve this problem, we should do pose clustering first and then within each dasiapose grouppsila, clustering images of different individuals. Gabor filters have been used to detect the eyes in the face image. The coordinate of the eyes have been extracted as an input feature for the dasiapose clusteringpsila. After doing this, images of the similar pose will be in the same cluster. PCA/ LBP and kmeans algorithms have been used in each pose cluster for clustering of different individuals. The precision of face classification with clustering is enhanced. The proposed clustering algorithms can be applied to and face indexing or face recognition system.