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Although various methods based on the hand-crafted features and deep learning methods have been developed for various applications in the past few years, distinguishing untrained identities in testing phase still remains a challenging task. To overcome these difficulties, we propose a novel representation learning approach to unconstrained face verification and open-world person re-identification tasks. Our approach aims to reinforce the discriminative power of learned features by assigning the weight to each training sample. We demonstrate the efficiency of the proposed method by testing on datasets which are publicly available. The experimental results for both face verification and person re-identification tasks show that its performance is comparable to state-of-the-art methods based on hand-crafted feature and general convolutional neural network.

Unconstrained Face Verification and Open-World Person Re-identification via Densely-connected Convolution Neural Network