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As a unique biometric feature that can be recognized at a distance, gait has broad applications in crime prevention, forensic identification and social security. To portray a gait, existing gait recognition methods utilize either a gait template, where temporal information is hard to preserve, or a gait sequence, which must keep unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames. We propose a new network named GaitSet to learn identity information from the set. Based on the set perspective, our method is immune to permutation of frames, and can naturally integrate frames from different videos which have been filmed under different scenarios, such as diverse viewing angles, different clothes/carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results represent new state-of-the-art recognition accuracy. On various complex scenarios, our model exhibits a significant level of robustness. It achieves accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing walking conditions, respectively. These outperform the existing best methods by a large margin. The method presented can also achieve a satisfactory accuracy with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with only 7 frames. The source code has been released at https://github.com/AbnerHqC/GaitSet.

Superhydrophobic and superoleophilic polyester materials are successfully prepared by one‐step growth of silicone nanofilaments onto the textile via chemical vapor deposition of trichloromethylsilane. The successful growth of silicone nanofilaments is confirmed with scanning electron microscopy, energy‐dispersive X‐ray analysis, and investigation of the wetting behavior of water on the textile. Even microfibers deeply imbedded inside a woven material could be coated very well with the nanofilaments. The coated textile is water repellant and could only be wetted by liquids of low surface tension. The applications of the coated textile as a membrane for oil/water separation and as a bag for selective oil absorption from water are studied in detail. Owing to the superwetting properties and flexibility of the coated textile, excellent reusability, oil/water separation efficiency, and selective oil absorption capacity are observed, which make it very promising material, e.g., for practical oil absorption.

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