Using deep neural networks to learn effective feature representations has become popular in face recognition ［12， 20， 17， 22， 14， 13， 18， 21， 19， 15］。 With better deep network architectures and supervisory methods， face recognition accuracy has been boosted rapidly in recent years. In particular， a few noticeable face representation learning techniques are evolved recently. An early effort of learning deep face representation in a supervised way was to employ face verification as the supervisory signal ［12］， which required classifying a pair of training images as being the same person or not. It greatly reduced the intra-personal variations in the face representation. Then learning discriminative deep face representation through large-scale face identity classification （face identification） was proposed by DeepID ［14］ and DeepFace ［17， 18］。 By classifying training images into a large amount of identities， the last hidden layer of deep neural networks would form rich identity-related features. With this technique， deep learning got close to human performance for the first time on tightly cropped face images of the extensively evaluated LFW face verification dataset ［6］。 However， the learned face representation could also contain significant intrapersonal variations. Motivated by both ［12］ and ［14］， an approach of learning deep face representation by joint face identification-verification was proposed in DeepID2 ［13］ and was further improved in DeepID2+ ［15］。 Adding verification supervisory signals significantly reduced intrapersonal variations， leading to another significant improvement on face recognition performance. Human face verification accuracy on the entire face images of LFW was surpassed finally ［13， 15］。 Both GoogLeNet ［16］ and VGG ［10］ ranked in the top in general image classification in ILSVRC 2014. This motivates us to investigate whether the superb learning capacity brought by very deep net structures can also benefit face recognition.