TY - GEN
T1 - A Three-Category Face Detector with Contextual Information on Finding Tiny Faces
AU - Jiang, Feng
AU - Zhang, Jie
AU - Yan, Liping
AU - Xia, Yuanqing
AU - Shan, Shiguang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - Great progresses have been achieved on object detection in the wild. However, it still remains a challenging problem due to tiny objects. In this paper, we present a Three-category Classification Neural Network to find tiny faces under complex environments by leveraging contextual information around faces. Tiny faces (within 20×20 pixels) are so fuzzy that the facial patterns are not clear or even ambiguous for detection. To solve this problem, instead of formulating the face detection as a two-category classification task, a novel face detection network is proposed for three-category classification, i.e., normal face, tiny face and background. Moreover, we take full advantage of contextual information around faces and pick good prior anchors to predict good detection on tiny faces. Extensive experiments on two challenging face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of our method.
AB - Great progresses have been achieved on object detection in the wild. However, it still remains a challenging problem due to tiny objects. In this paper, we present a Three-category Classification Neural Network to find tiny faces under complex environments by leveraging contextual information around faces. Tiny faces (within 20×20 pixels) are so fuzzy that the facial patterns are not clear or even ambiguous for detection. To solve this problem, instead of formulating the face detection as a two-category classification task, a novel face detection network is proposed for three-category classification, i.e., normal face, tiny face and background. Moreover, we take full advantage of contextual information around faces and pick good prior anchors to predict good detection on tiny faces. Extensive experiments on two challenging face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of our method.
KW - Contextual information
KW - Face detector
KW - Three-category classification
KW - Tiny face
UR - http://www.scopus.com/inward/record.url?scp=85062897242&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2018.8451456
DO - 10.1109/ICIP.2018.8451456
M3 - Conference contribution
AN - SCOPUS:85062897242
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2680
EP - 2684
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
T2 - 25th IEEE International Conference on Image Processing, ICIP 2018
Y2 - 7 October 2018 through 10 October 2018
ER -