Generalized Adversarial and Hierarchical Co-occurrence Network based Synthetic Skeleton Generation and Human Identity Recognition

Human skeleton data provides a compact, low noise representation of relative joint locations that may be used in human identity and activity recognition. Hierarchical Co-occurrence Network (HCN) has been used for human activity recognition because of its ability to consider correlation between joints in convolutional operations in the network. HCN shows good identification accuracy but requires a large number of samples to train. Acquisition of this large-scale data can be time consuming and expensive, motivating synthetic skeleton data generation for data augmentation in HCN. We propose a novel method that integrates an Auxiliary Classifier Generative Adversarial Network (AC-GAN) and HCN hybrid framework for Assessment and Augmented Identity Recognition for Skeletons (AAIRS). The proposed AAIRS method performs generation and evaluation of synthetic 3-dimensional motion capture skeleton videos followed by human identity recognition. Synthetic skeleton data produced by the generator component of the AC-GAN is evaluated using an Inception Score-inspired realism metric computed from the HCN classifier outputs. We study the effect of increasing the percentage of synthetic samples in the training set on HCN performance. Before synthetic data augmentation, we achieve 74.49% HCN performance in 10-fold cross validation for 9-class human identification. With a synthetic-real mixture of 50%-50%, we achieve 78.22% mean accuracy, significantly (p<0.05) outperforming the baseline HCN performance. The proposed framework demonstrates the feasibility of combining a synthetic data generation architecture with hierarchical co-occurrence feature learning for human identity recognition.