https://scholars.lib.ntu.edu.tw/handle/123456789/607195
標題: | Prominent facial feature and hybrid learning method-based advanced face detector robust to head-up, head-down, and arbitrary rotation cases | 作者: | Chen C.-Y JIAN-JIUN DING HW Hsu |
關鍵字: | Deep learning;Morphological operations;Prominent facial features;Refocus;View-invariant face detection;Convolutional neural networks;Feature extraction;Learning systems;Rotation;Arbitrary rotation;Color information;Detection rates;Face database;Facial feature;Hybrid learning methods;Profile faces;Prominent features;Face recognition | 公開日期: | 2021 | 卷: | 15 | 期: | 1 | 起(迄)頁: | 147-154 | 來源出版物: | Signal, Image and Video Processing | 摘要: | Face detection is important for computer vision. Until now, the Viola and Jones algorithm is still the most popular face detector embedded in digital cameras. Although it has good performance in frontal face detection, it may not perform well in detecting rotated, head-up, and head-down faces. In this paper, we propose a more robust face detector based on prominent feature extraction. We find that, in addition to using eyes and mouth, which have been adopted by other algorithms, noses and ears are also important clues for face detection. Particularly, for a profile face, only a single eye can be detected. For head-up or head-down faces, eyes and mouth may not be well detected. However, in these cases, noses and ears can still be detected well and we can apply these features to improve the accuracy of rotated, head-up, and head-down face detection. To well detect these prominent features, in addition to the Viola–Jones detector, we also apply edge and color information and use the refocus algorithm based on the convolutional neural network. Simulations conducted on several popular multi-view face databases show that the proposed face detector can attain higher detection rates and is robust to rotated, head-up, and head-down cases. ? 2020, Springer-Verlag London Ltd., part of Springer Nature. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087682475&doi=10.1007%2fs11760-020-01729-w&partnerID=40&md5=6e67e4a7d226429b59507885e62a9ccf https://scholars.lib.ntu.edu.tw/handle/123456789/607195 |
ISSN: | 18631703 | DOI: | 10.1007/s11760-020-01729-w |
顯示於: | 電機工程學系 |
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