Margin Emphasized Metric Learning and its application to Gabor feature based face recognition

Shaoxin Li, Shiguang Shan

Abstract: In addressing side information based face recognition scenario, a new Margin Emphasized Metric Learning (MEML) method is proposed. As an improvement of previous metric learning, MEML defines a new objective function for optimization, which adds more weights to sample pairs on the boundary thus hard to classify. To further improve face verification performance, MEML is applied to Gabor feature in a block dividing and combining mode. Experiments on LFW image-restricted setting illustrate very impressive performance compared with traditional methods. By combining multiple MEML classifiers on several features, performance comparable to the best known results on LFW is achieved.