Abstract
Human biometrics recognition has been of wide interest recently due to its benefits in various applications such as health care and recommender systems. The rise of deep learning development, together with the massive data acquisition systems, made it feasible to reuse models trained on one task for solving another similar task. In this work, we present a novel approach for age and gender recognition based on gait data acquired from Inertial Measurement Unit (IMU). BioDeep design is composed of two phases, first of which is applying a statistical method for feature modelling, the autocorrelation function, then building a Convolutional Neural Network (CNN) for age regression and gender classification. We also use random forest as a baseline model to compare the results achieved by both methods. We validate our models using four publicly available datasets. The second phase is doing transfer learning over these diverse datasets. We train a CNN on one dataset and reuse its feature maps over the other datasets for solving both age and gender recognition problems. Our experimental evaluation over the four datasets separately shows very promising results. Furthermore, transfer learning achieved 20 30x speedup in the training time in addition to keeping the acceptable prediction accuracy.