GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning
Huanzhang Dou Pengyi Zhang Yuhan Zhao Lin Dong Zequn Qin Xi Li
Gait recognition aims at identifying the pedestrians at a long distance by
their biometric gait patterns. It is inherently challenging due to the various
covariates and the properties of silhouettes (textureless and colorless), which
result in two kinds of pair-wise hard samples: the same pedestrian could have
distinct silhouettes (intra-class diversity) and different pedestrians could
have similar silhouettes (inter-class similarity). In this work, we propose to
solve the hard sample issue with a Memory-augmented Progressive Learning
network (GaitMPL), including Dynamic Reweighting Progressive Learning module
(DRPL) and Global Structure-Aligned Memory bank (GSAM). Specifically, DRPL
reduces the learning difficulty of hard samples by easy-to-hard progressive
learning. GSAM further augments DRPL with a structure-aligned memory mechanism,
which maintains and models the feature distribution of each ID. Experiments on
two commonly used datasets, CASIA-B and OU-MVLP, demonstrate the effectiveness
of GaitMPL. On CASIA-B, we achieve the state-of-the-art performance, i.e.,
88.0% on the most challenging condition (Clothing) and 93.3% on the average
condition, which outperforms the other methods by at least 3.8% and 1.4%,
respectively.