CNN 기반의 와일드 환경에 강인한 고속 얼굴 검출 방법 Fast and Robust Face Detection based on CNN in Wild Environment

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 436
  • Download : 0
Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.
Publisher
한국멀티미디어학회
Issue Date
2016-08
Language
Korean
Citation

멀티미디어학회논문지, v.19, no.8, pp.1310 - 1319

ISSN
1229-7771
DOI
10.9717/kmms.2016.19.8.1310
URI
http://hdl.handle.net/10203/213551
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0