Abstract: A two-stage person searching method combining face and appearance features, comprises: detecting a face of a person utilizing a face detector, and outputting a face representation vector based on a face comparing model; ranking person sets to be matched according to an Euclidean distance to acquire a face ranking result; selecting a plurality of samples as multi-matching targets at the next stage according to the ranking result; using the selected multi-matching targets of different persons at the next stage in the same data set as mutual negative samples, so as to compress the sample space matched at the next stage; and finally re-recognizing multi-target persons, and ranking the image sets to be matched according to an average distance or similarity with multiple targets to output a final result.

Abstract: A two-stage person searching method combining face and appearance features, comprises: detecting a face of a person utilizing a face detector, and outputting a face representation vector based on a face comparing model; ranking person sets to be matched according to an Euclidean distance to acquire a face ranking result; selecting a plurality of samples as multi-matching targets at the next stage according to the ranking result; using the selected multi-matching targets of different persons at the next stage in the same data set as mutual negative samples, so as to compress the sample space matched at the next stage; and finally re-recognizing multi-target persons, and ranking the image sets to be matched according to an average distance or similarity with multiple targets to output a final result.