Many algorithms for semantic person retrieval suffer from a lack of training data often due to the difficulties in constructing a large dataset. We therefore propose a transfer learning based approach for semantic person identification and semantic person search. We apply the fine-tuned Mask R-CNN and DenseNet-161 for detection and attribute classification. The networks were pre-trained on the MS COCO and ILSVRC 2012 datasets. Our proposed approach achieves the highest recognition rate at each rank of CMC curve for semantic person identification and the highest average localization precision for semantic person search on our validation dataset.