One of the present approaches to gait recognition exploits the signals captured by wearable sensors, especially the accelerometers embedded in modern smartphones. However, the different speed, the ground slope, or simply the time lapse between captures cause variations that negatively affect long term recognition in a dramatic way. The proposed procedure aims at extracting gait characteristics that are as invariant as possible, and therefore useful for accurate long term recognition. The experiments compare the performance of the proposal with others in state-of-the-art that use the same benchmark, namely the ZJU-gaitacc dataset. This dataset includes a high number of samples per subject, captured in two time-separated sessions. This allows to assess the performance of the proposed method also in the long term, i.e., when comparing templates captured in different times. Most works using the same benchmark so far have not exploited both sessions. They use samples captured in the same time, constraining the use of this trait to continuous recognition, e.g., of the smartphone owner. The obtained results testify that, in this condition, the proposed feature-based method outperforms competitors in the current literature. The experiments also compare the results from a session-based partition with those obtained from a training that mixes-up samples from different sessions. As expected, the latter strategy can dramatically improve the measured performance. The significantly different results seem to suggest that the session-based partition, when feasible, can provide more realistic results, closer to the real-world application context when behavioural traits are involved in the medium/long term. The same results seem also to testify that there is still need to improve the accuracy of gait recognition via wearable sensors. This calls for further investigation of the problems related to the variability over time in the pattern of individual gait signals.
Towards the suitability of gait wearable signal processing for long term recognition / De Marsico, M.; Palermo, A.. - (2022), pp. 1-9. (Intervento presentato al convegno 2022 IEEE International Joint Conference on Biometrics, IJCB 2022 tenutosi a Abu Dhabi - United Arab Emirates) [10.1109/IJCB54206.2022.10007932].
Towards the suitability of gait wearable signal processing for long term recognition
De Marsico M.Membro del Collaboration Group
;Palermo A.Membro del Collaboration Group
2022
Abstract
One of the present approaches to gait recognition exploits the signals captured by wearable sensors, especially the accelerometers embedded in modern smartphones. However, the different speed, the ground slope, or simply the time lapse between captures cause variations that negatively affect long term recognition in a dramatic way. The proposed procedure aims at extracting gait characteristics that are as invariant as possible, and therefore useful for accurate long term recognition. The experiments compare the performance of the proposal with others in state-of-the-art that use the same benchmark, namely the ZJU-gaitacc dataset. This dataset includes a high number of samples per subject, captured in two time-separated sessions. This allows to assess the performance of the proposed method also in the long term, i.e., when comparing templates captured in different times. Most works using the same benchmark so far have not exploited both sessions. They use samples captured in the same time, constraining the use of this trait to continuous recognition, e.g., of the smartphone owner. The obtained results testify that, in this condition, the proposed feature-based method outperforms competitors in the current literature. The experiments also compare the results from a session-based partition with those obtained from a training that mixes-up samples from different sessions. As expected, the latter strategy can dramatically improve the measured performance. The significantly different results seem to suggest that the session-based partition, when feasible, can provide more realistic results, closer to the real-world application context when behavioural traits are involved in the medium/long term. The same results seem also to testify that there is still need to improve the accuracy of gait recognition via wearable sensors. This calls for further investigation of the problems related to the variability over time in the pattern of individual gait signals.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
