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- W2918711259 abstract "Face detection is one of the first stages of face recognition systems. Thanks to advances in deep neural networks, success has been achieved on many similar image recognition tasks. When videos are available, temporal information can be used for determining the position of the face, avoiding having to detect faces for all frames. Such techniques can be applied in in-the-wild environments where current face detection methods fail to perform robustly. To address these limitations, this work explores an original approach, tracking the nose region initialized based on face quality analysis. A quality score is calculated for assisting the nose tracking initialization, avoiding depending on the first frame, in which may contain degraded data. The nose region, rather than the entire face was chosen due to it being unlikely to be occluded, being mostly invariant to facial expressions, and being visible in a long range of head poses. Experiments performed on the 300 Videos in the Wild and Point and Shoot Challenge datasets indicate nose tracking is a useful approach for in-the-wild scenarios." @default.
- W2918711259 created "2019-03-11" @default.
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- W2918711259 date "2019-01-01" @default.
- W2918711259 modified "2023-09-26" @default.
- W2918711259 title "Nose Based Rigid Face Tracking" @default.
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- W2918711259 doi "https://doi.org/10.1007/978-3-030-13469-3_65" @default.
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