Face recognition using the POEM descriptor
Résumé
Real-world face recognition systems require careful balancing of three concerns: computational cost, robustness, and discriminative power. In this paper we describe a new descriptor, POEM (Patterns of Oriented Edge Magnitudes) by applying a self-similarity based structure on oriented magnitudes and prove that it addresses all three criteria. Experimental results on the FERET databases show that POEM outperforms other descriptors when used with nearest neighbour classifiers. With LFW by combining POEM with GMMs and with multi-kernel SVMs, we achieve comparable results to the state of the art. Impressively, POEM is around 20 times faster than Gabor-based methods