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Ajian Liu; Zichang Tan; Jun Wan; Sergio Escalera; Guodong Guo; Stan Z. Li |
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Title  |
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-Ethnicity Face Anti-Spoofing |
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Conference Article |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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1178-1186 |
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The issue of ethnic bias has proven to affect the performance of face recognition in previous works, while it still remains to be vacant in face anti-spoofing. Therefore, in order to study the ethnic bias for face anti-spoofing, we introduce the largest CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, covering 3 ethnicities, 3 modalities, 1,607 subjects, and 2D plus 3D attack types. Five protocols are introduced to measure the affect under varied evaluation conditions, such as cross-ethnicity, unknown spoofs or both of them. As our knowledge, CASIA-SURF CeFA is the first dataset including explicit ethnic labels in current released datasets. Then, we propose a novel multi-modal fusion method as a strong baseline to alleviate the ethnic bias, which employs a partially shared fusion strategy to learn complementary information from multiple modalities. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability for other existing datasets, i.e., CASIA-SURF, OULU-NPU and SiW datasets. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2020?authuser=0. |
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Virtual; January 2021 |
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WACV |
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HUPBA; no proj |
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no |
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Admin @ si @ LTW2021 |
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3661 |
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Author |
Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen |
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Title |
Bandwidth Limited Object Recognition in High Resolution Imagery |
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Conference Article |
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Year |
2017 |
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IEEE Winter conference on Applications of Computer Vision |
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This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance. |
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Santa Rosa; CA; USA; March 2017 |
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LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 |
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Call Number |
Admin @ si @ LBW2017 |
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2973 |
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