TY - GEN
T1 - Multiple thermal face detection in unconstrained environments using fully convolutional networks
AU - Fan, Yezhao
AU - Zhai, Guangtao
AU - Wang, Jia
AU - Hu, Menghan
AU - Liu, Jing
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Multiple thermal face detection in unconstrained environments has received increasing attention due to its potential in liveness detection and night-time surveillance. This paper presents an effective method based on fully convolutional network (FCN), density-based spatial clustering of applications with noise (DBSCAN) and non-maximum suppression (NMS) algorithm. Our proposed approach captures the thermal face features automatically using FCN. Then, an improved DBSCAN is used to detect all the faces in the thermal images. Finally, we use NMS to remove all of the bounding-boxes with an IOU (intersection over union). Experiments on RGB-D-T database show that the proposed method exceeds the state-of-the-art algorithms for single face detection on thermal images. We also build a new database with 10K multiple thermal face images in unconstrained environments. The results also show a high precision for multi-face detection tasks.
AB - Multiple thermal face detection in unconstrained environments has received increasing attention due to its potential in liveness detection and night-time surveillance. This paper presents an effective method based on fully convolutional network (FCN), density-based spatial clustering of applications with noise (DBSCAN) and non-maximum suppression (NMS) algorithm. Our proposed approach captures the thermal face features automatically using FCN. Then, an improved DBSCAN is used to detect all the faces in the thermal images. Finally, we use NMS to remove all of the bounding-boxes with an IOU (intersection over union). Experiments on RGB-D-T database show that the proposed method exceeds the state-of-the-art algorithms for single face detection on thermal images. We also build a new database with 10K multiple thermal face images in unconstrained environments. The results also show a high precision for multi-face detection tasks.
KW - Density-based spatial clustering of applications with noise
KW - Fully convolutional network
KW - Intersection over union
KW - Multiple thermal face detection
KW - Nonmaximum suppression
UR - https://www.scopus.com/pages/publications/85047462310
U2 - 10.1007/978-3-319-77383-4_3
DO - 10.1007/978-3-319-77383-4_3
M3 - 会议稿件
AN - SCOPUS:85047462310
SN - 9783319773827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 24
EP - 33
BT - Advances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
A2 - Zeng, Bing
A2 - Li, Hongliang
A2 - Huang, Qingming
A2 - El Saddik, Abdulmotaleb
A2 - Jiang, Shuqiang
A2 - Fan, Xiaopeng
PB - Springer Verlag
T2 - 18th Pacific-Rim Conference on Multimedia, PCM 2017
Y2 - 28 September 2017 through 29 September 2017
ER -