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Face Recognition has been studied for many decades. As opposed to traditionalhand-crafted features such as LBP and HOG, much more sophisticated features canbe learned automatically by deep learning methods in a data-driven way. In thispaper, we propose a two-stage approach that combines a multi-patch deep CNN anddeep metric learning, which extracts low dimensional but very discriminativefeatures for face verification and recognition. Experiments show that thismethod outperforms other state-of-the-art methods on LFW dataset, achieving99.77% pair-wise verification accuracy and significantly better accuracy underother two more practical protocols. This paper also discusses the importance ofdata size and the number of patches, showing a clear path to practicalhigh-performance face recognition systems in real world.

Targeting Ultimate Accuracy: Face Recognition via Deep Embedding