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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper's core finding is that combining an identity classification task as well as metric-learning-style verification task helps to learn better features for face classification/verification. The verification task here tries to decrease feature-space distance between instances of the same identity, and increase distance between those of different identities. This improvement is embedded in a state-of-the-art system for face verification, which uses convnets trained on many (400) different views to generate features, distilled into a small set of 25 using feature selection. Very good results are obtained and experiments performed using LFW as a test set. Overall, these are very good results obtained using a somewhat complex pipeline, and a good investigation into the contribution of each task in the loss for feature learning.