HLFNet: High-low Frequency Network for Person Re-Identification

Person re-identification (re-ID) technology has attracted many scholars in the past few years. With the recent developments of deep learning technology, person re-ID has been greatly improved. However, the main chalenge of re-ID is to distinguish the detailed information in different images. Consequently, it is of significant importance to extract fine-grained features in the re-ID tasks. In the present study, a novel method, called the high-low frequency network (HLFNet), is proposed to effectively use the image information of different frequencies and focus on the detailed information between different individual images. In this regard, high frequency and low-frequency information are initially extracted from the original image, and then two backbones are applied to extract the features from the two information branches. Different frequencies of image information complement each other so that a better recognition effect can be achieved. Moreover, a local branch is utilized to extract the distinguishable local features for guiding the global feature branch in the training stage. Finally, only the extracted global feature from the trained network is required in the inference phase of re-ID. Performed experiments demonstrate that the proposed method can significantly enhance the feature representation accuracy and achieve the state-of-the-art performance on diverse benchmarks.