Person re-identification(Re-ID) technology has been a research hotspot in intelligent video surveillance, which accurately retrieves specific pedestrians from massive video data. Most research focuses on the short-term scenarios of person Re-ID to deal with general problems, such as occlusion, illumination change, and view variance. The appearance change or similar appearance problem in the long-term scenarios has has not been the focus of past research. This paper proposes a novel Re-ID framework consisting of a two-branch model to fuse the appearance and gait feature to overcome covariate changes. Firstly, we extract the appearance features from a video sequence by ResNet50 and leverage average pooling to aggregate the features. Secondly, we design an improved gait representation to obtain a person’s motion information and exclude the effects of external covariates. Specifically, we accumulate the difference between silhouettes to form an active energy image (AEI) and then mask the mid-body part in the image with the Improved-Sobel-Masking operator to extract the final gait representation called ISMAEI. Thirdly, we combine appearance features with gait features to generate discriminative and robust fused features. Finally, the Euclidean norm is adopted to calculate the distance between probe and gallery samples for person Re-ID. The proposed method is evaluated on the CASIA Gait Database B and TUM-GAID datasets. Compared with state-of-the-art methods, experimental results demonstrate that it can perform better in both Rank-1 and mAP.

Human-centered intelligent human–robot interaction can transcend the traditional keyboard and mouse and have the capacity to understand human communicative intentions by actively mining implicit human clues (e.g., identity information and emotional information) to meet individuals’ needs. Gait is a unique biometric feature that can provide reliable information to recognize emotions even when viewed from a distance. However, the insufficient amount and diversity of training data annotated with emotions severely hinder the application of gait emotion recognition. In this paper, we propose an adversarial learning framework for emotional gait dataset augmentation, with which a two-stage model can be trained to generate a number of synthetic emotional samples by separating identity and emotion representations from gait trajectories. To our knowledge, this is the first work to realize the mutual transformation between natural gait and emotional gait. Experimental results reveal that the synthetic gait samples generated by the proposed networks are rich in emotional information. As a result, the emotion classifier trained on the augmented dataset is competitive with state-of-the-art gait emotion recognition works.

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