People Identification Based on Soft Biometrics Features Obtained from 2D Poses
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An important challenge in the research field of Biometrics is real-time identification, at a distance, in uncontrolled environments, using low-resolution cameras. In such circumstances, soft biometrics can be the only option. In this work, we propose two novel descriptor methods for biometric identification based on ensemble of anthropometric measurements and joints heat-map of the person skeleton, captured from video frames through state-of-the-art 2D poses estimation methods. The proposed methods were assessed on a popular benchmark dataset, CASIA Gait Dataset B, and obtained good results (85% and 89% of rank-1 identification rates, respectively) with PifPaf 2D pose estimation method.