Individuality-preserving Silhouette Extraction for Gait Recognition and Its Speedup
| 言語 | 英語 |
|---|---|
| 種類 | 論文 |
| 著者 | Masakazu Iwamura，Shunsuke Mori，Koichiro Nakamura，Takuya Tanoue，Yuzuko Utsumi，Yasushi Makihara，Daigo Muramatsu，Koichi Kise，Yasushi Yagi |
| 論文名 | Individuality-preserving Silhouette Extraction for Gait Recognition and Its Speedup |
| 論文誌名 | IEICE Transactions on Information and Systems |
| Vol. | E104-D |
| No. | 7 |
| ページ | pp.992-1001 |
| 査読の有無 | 有 |
| 年月 | 2021年7月 |
| 分野 | パターン認識 |
| DOI | 10.1587/transinf.2020ZDP7500 |
| キー | Iwamura2021_IEICE (id=1820) |
要約
Most gait recognition approaches rely on silhouette-based representations due to high recognition accuracy and computational efficiency. A fundamental problem for those approaches is how to extract individuality-preserved silhouettes from real scenes accurately. Foreground colors may be similar to background colors, and the background is cluttered. Therefore, we propose a method of individuality-preserving silhouette extraction for gait recognition using standard gait models (SGMs) composed of clean silhouette sequences of various training subjects as shape priors. The SGMs are smoothly introduced into a well-established graph-cut segmentation framework. Experiments showed that the proposed method achieved better silhouette extraction accuracy by more than 2.3% than representative methods and better identification rate of gait recognition (improved by more than 11.0% at rank 20). Besides, to reduce the computation cost, we introduced approximation in the calculation of dynamic programming. As a result, without reducing the segmentation accuracy, we reduced 85.0% of the computational cost.
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