Person Re-Identification With Character-Illustration-Style Image and Normal Photo

Retrieving the given objects hidden amidst the gallery set is important for public safety and decision-making. Heterogeneous pedestrian retrieval (person re-identification) aims to retrieve the same person images from different modality set for identification. To address this problem, we contribute a new ${c}$ haracter-illustration-style image and ${n}$ ormal photo ${p}$ edestrian re- ${i}$ dentification ${d}$ ataset (CINPID), which is collected on campus. The CINPID dataset includes two modalities, i.e., normal photos captured by one camera and character-illustration-style images drawn by the painter. To handle the problem of pedestrian retrieval with character-illustration-style images and normal photos, we propose a semi-coupled mapping and discriminant dictionary learning (SMD2L), which can learn a semi-coupled mapping matrix and dictionary pair from heterogeneous samples. With the learned semi-coupled mapping matrix, the differences between heterogeneous data can be reduced to some extent. Experimental results on the new CINPID dataset show that our approach outperforms the compared competing methods.