Novel Body Biometric for Long-Range Recognition Under Extreme Conditions

The task of video-based human recognition is complicated by many factors such as imaging distortions, imaging range, lack of frames, arbitrary pose, occlusions, air turbulence, and changing clothes. This work presents the first study that utilizes single-frame binary silhouettes and their auxiliary representations for human recognition under extreme distortions. The proposed representation is compact, modular, and robust to distortions, allowing for easy deployability for long-range recognition. Quantitative metrics are reported on long-range dataset such as Briar, demonstrating the robustness of the proposed approach to common challenges of video-based recognition in the wild. The proposed single-frame method is compared against gait techniques using limited frames, outperforming in most cases. Performance is also compared to grayscale images with varying ranges, environments, and changing clothes, where the proposed model outperforms grayscale images. Under consistent conditions, the proposed model still augments the performance of the baseline grayscale model by over 15%.