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Improving Face Recognition Methods based on POEM Features

: POEM descriptors has been successfully used for face recognition. The usual way how the descriptor is utilized consists in constructing POEM features in the rectangular non-overlapping regions covering the whole image. The features created in the regions are then concatenated into one long vector representing the face. We propose an enhancement of this method using automatic key-point identiﬁcation strategies. In our approach, the image features are created in the detected key-points. We also employ a more complex matching procedure that compares the features individually. This method is efﬁcient particularly when the number of training samples is small and therefore neural network based methods fail, because they do not have enough training data. The proposed approach is evaluated on three standard face corpora. We also study the inﬂuence of several parameters of the method on the overall performance. The obtained results show that the combination of POEM features with the automatic point identiﬁcation and a more sophisticated matching algorithm brings signiﬁcant improvement over the baseline method.