  We address the problem of person re-identification from commodity depth
sensors. One challenge for depth-based recognition is data scarcity. Our first
contribution addresses this problem by introducing split-rate RGB-to-Depth
transfer, which leverages large RGB datasets more effectively than popular
fine-tuning approaches. Our transfer scheme is based on the observation that
the model parameters at the bottom layers of a deep convolutional neural
network can be directly shared between RGB and depth data while the remaining
layers need to be fine-tuned rapidly. Our second contribution enhances
re-identification for video by implementing temporal attention as a
Bernoulli-Sigmoid unit acting upon frame-level features. Since this unit is
stochastic, the temporal attention parameters are trained using reinforcement
learning. Extensive experiments validate the accuracy of our method in person
re-identification from depth sequences. Finally, in a scenario where subjects
wear unseen clothes, we show large performance gains compared to a
state-of-the-art model which relies on RGB data.
