People identification using gait information (i.e., the way a person walks) obtained from inertial sensors is a robust approach that can be used in multiple situations where vision-based systems are not applicable. Typically, previous methods use hand-crafted features or deep learning approaches with pre-processed features as input. In contrast, we present a new deep learning-based end-to-end approach that employs raw inertial data as input. By this way, our approach is able to automatically learn the best representations without any constraint introduced by the pre-processed features. Moreover, we study the fusion of information from multiple inertial sensors and multi-task learning from multiple labels per sample. Our proposal is experimentally validated on the challenging dataset OU-ISIR, which is the largest available dataset for gait recognition using inertial information. After conducting an extensive set of experiments to obtain the best hyper-parameters, our approach is able to achieve state-of-the-art results. Specifically, we improve both the identification accuracy (from 83.8% to 94.8%) and the authentication equal-error-rate (from 5.6 to 1.1). Our experimental results suggest that: 1) the use of hand-crafted features is not necessary for this task as deep learning approaches using raw data achieve better results; 2) the fusion of information from multiple sensors allows to improve the results; and, 3) multi-task learning is able to produce a single model that obtains similar or even better results in multiple tasks than the corresponding models trained for a single task.

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In this work we propose a new method to detect arbitrary planar shapes from a previous template and calculate the parameters that define the transformations between the new image and the template. The image contains a perspective projection of the template subjected to two angles transformation, called tilt and pan, a displacement a rotation and a scaling. The method uncouples parameter calculation to improve computational requirements by comparing invariant information from the template and the image. The Generalized Hough Transform is used to compare this information and to vote into a parameter space.