Proceedings Article10.1109/ICPR.2014.16
Deep Metric Learning for Person Re-identification
Dong Yi,Zhen Lei,Shengcai Liao,Stan Z. Li +3 more
- 24 Aug 2014
- pp 34-39
1.1K
TL;DR: A more general way that can learn a similarity metric from image pixels directly by using a "siamese" deep neural network that can jointly learn the color feature, texture feature and metric in a unified framework is proposed.
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Abstract: Various hand-crafted features and metric learning methods prevail in the field of person re-identification Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly By using a "siamese" deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework The network has a symmetry structure with two sub-networks which are connected by a cosine layer Each sub network includes two convolutional layers and a full connected layer To deal with the big variations of person images, binomial deviance is used to evaluate the cost between similarities and labels, which is proved to be robust to outliers Experiments on VIPeR illustrate the superior performance of our method and a cross database experiment also shows its good generalization
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