Person Re-Identification Based on DropEasy Method

Currently, majority of person re-identification (reID) technologies are network-constrained by Dropout regularization, which relies on the random zeroing out of some features to make these features more independent. However, such random zeroing regularization methods are not very effective for improving network performance, because they neglect the unique contribution of different features to the network performance. To improve the value of indiscriminative features in network training, a DropEasy-based person reID method is proposed in this paper. Features are classified into discriminative and indiscriminative ones, according to the distance between the feature vectors of positive or negative sample pairs wherein the discriminative features are zeroed out, while the indiscriminative features are reserved, and the network only learns through indiscriminative features. Furthermore, because networks are always inclined to make up for incomplete information by drawing on the surrounding features in the feature maps, Dropout loses its effectiveness for the network-constraints. To solve this challenge, the DropEasy2d method that can be effectively applied to convolutional layers is further proposed in this paper. DropEasy2d searches discriminative feature areas in the feature maps by sliding and zeroing windows while reserving the indiscriminative features areas to constrain network learning. The effectiveness of the proposed method is demonstrated using the Market-1501, DuckMTMC-reID, and CUHK03 datasets. For example, in the Market-1501 dataset, DropEasy can improve the mean average precision (mAP) and Rank-1 accuracy of the ID-discriminative embedding (IDE) to 72.7%(+8.8%) and 90.5%(+6.8%), respectively, while DropEasy2d can raise them to 68.5%(+4.6%) and 88.7%(+5.0%), respectively. Based on the results, the proposed method can improve the network performance during the extraction and generalization of the discriminative features.