Ricerc@Sapienza

This paper presents an approach to gait recognition based on a single consumer accelerometer, built in most present mobile devices. It does not propose a completely novel
algorithm, but rather investigates better ways to exploit the Dynamic TimeWarping (DTW), which is still one of the most used at present in literature. To this aim, the paper presents both a new segmentation algorithm to split the gait signal into cycles/steps, and investigates the best way to use the possibly segmented signal for recognition. Summarizing, the first contribution of the present work is the proposal of a new segmentation algorithm for the gait signal, which does not require any pre-processing, either interpolation or noise reduction, to enhance the original signal, and its comparison with two other state-of-the-art step segmentation algorithms. The second contribution is related to the extensive tests performed with the five different investigated matching methods. The tests are carried out exploiting all compared segmentation algorithms and three different datasets, collected using different sensors: the originally exploited BWR dataset, that includes walk templates from 30
volunteers, and two huge datasets used for this kind of testing, namely the ZJU-gaitacc and the OU-ISIR Inertial Sensor Database. Tests have been performed in both verification
mode, either single-template or multiple-template, and identification mode, both closed and open set. The latter is rarely found in literature though representing the most frequently predictable applicative setting. It is worth underlining that the final goal is to allow using low-cost, built-in sensors that nowadays equip most smartphones. The best result in closed set identification, which is the identification mode usually reported in literature, is achieved using the most constrained method, i.e., limiting the walks in the gallery and in the probe to have a similar number of steps. It reaches ? 93 % of Recognition Rate (RR) on ZJUgaitacc dataset. The best result obtained with methods exploiting segmentation to overcome the mentioned limitation reaches ? 83 % of Recognition Rate (RR) on the same dataset, using our proposed algorithm. The best results in verification is achieved using multiple templates per user, again without segmentation, with an Equal Error Rate (EER) of 0.09, while the best results with segmentation is achieved again with our algorithm and is and EER of 0.10. This is a very good result for a soft biometrics as gait if often considered. As expected, open set identification achieves lower performance.