Abstract
In this paper, we address face verification and person re-identification tasks under the unconstrained condition. Both tasks under the unconstrained condition are difficult since the testing dataset can contain the identities not appeared in training dataset. To overcome the difficulty, we propose deep discriminative representation learning (DDRL) to learn a discriminative representation which can cover not only trained representation but the appearance of images which are not trained. DDRL can be viewed as imposing discriminative constraints on the learnt representation via joint optimization for verification and identification objectives. The experimental results for face verification and person re-identification shows the superiority of DDRL in both tasks.
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