From facial parts responses to face detection: A deep learning approach
Refereed conference paper presented and published in conference proceedings

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摘要In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method [23] by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.
著者Yang S., Luo P., Loy C.-C., Tang X.
會議名稱15th IEEE International Conference on Computer Vision, ICCV 2015
會議開始日11.12.2015
會議完結日18.12.2015
會議地點Santiago
會議國家/地區智利共和國
詳細描述organized by IEEE,
出版年份2016
月份2
日期17
卷號11-18-December-2015
頁次3676 - 3684
國際標準書號9781467383912
國際標準期刊號1550-5499
語言英式英語

上次更新時間 2020-06-08 於 02:04