iLGaCo: Incremental Learning of Gait Covariate Factors

TL;DR

This paper introduces iLGaCo, an incremental learning method for gait recognition that updates models efficiently with new data without full retraining, maintaining high accuracy and outperforming existing methods.

Contribution

iLGaCo is the first incremental learning approach for gait covariate factors, enabling efficient model updates with minimal accuracy loss.

Findings

Achieves near-scratch training accuracy with less computation.

Outperforms LwF and iCarl in incremental gait recognition tasks.

Maintains accuracy with only partial retraining on new data.

Abstract

Gait is a popular biometric pattern used for identifying people based on their way of walking. Traditionally, gait recognition approaches based on deep learning are trained using the whole training dataset. In fact, if new data (classes, view-points, walking conditions, etc.) need to be included, it is necessary to re-train again the model with old and new data samples. In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset. Instead, our approach performs a shorter training process with the new data and a small subset of previous samples. This way, our model learns new information while retaining previous knowledge. We evaluate iLGaCo on CASIA-B dataset in two incremental ways: adding new view-points and adding new walking conditions. In both cases, our results are close to the classical `training-from-scratch' approach, obtaining a marginal drop in accuracy ranging from 0.2% to 1.2%, what shows the efficacy of our approach. In addition, the comparison of iLGaCo with other incremental learning methods, such as LwF and iCarl, shows a significant improvement in accuracy, between 6% and 15% depending on the experiment.