  Emotion recognition is an important part of affective computing. Extracting
emotional cues from human gaits yields benefits such as natural interaction, a
nonintrusive nature, and remote detection. Recently, the introduction of
self-supervised learning techniques offers a practical solution to the issues
arising from the scarcity of labeled data in the field of gait-based emotion
recognition. However, due to the limited diversity of gaits and the
incompleteness of feature representations for skeletons, the existing
contrastive learning methods are usually inefficient for the acquisition of
gait emotions. In this paper, we propose a contrastive learning framework
utilizing selective strong augmentation (SSA) for self-supervised gait-based
emotion representation, which aims to derive effective representations from
limited labeled gait data. First, we propose an SSA method for the gait emotion
recognition task, which includes upper body jitter and random spatiotemporal
mask. The goal of SSA is to generate more diverse and targeted positive samples
and prompt the model to learn more distinctive and robust feature
representations. Then, we design a complementary feature fusion network (CFFN)
that facilitates the integration of cross-domain information to acquire
topological structural and global adaptive features. Finally, we implement the
distributional divergence minimization loss to supervise the representation
learning of the generally and strongly augmented queries. Our approach is
validated on the Emotion-Gait (E-Gait) and Emilya datasets and outperforms the
state-of-the-art methods under different evaluation protocols.
