TY - GEN
T1 - SEFD
T2 - 2019 International Conference on Biometrics, ICB 2019
AU - Shi, Lei
AU - Xu, Xiang
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 2017-ST-BTI-0001-0201. This grant is awarded to the Borders, Trade, and Immigration (BTI) Institute: A DHS Center of Excellence led by the University of Houston, and includes support for the project EDGE awarded to the University of Houston.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Recently, the state-of-the-art face detectors are extending a backbone network by adding more feature fusion and context extractor layers to localize multi-scale faces. Therefore, they are struggling to balance the computational efficiency and performance of face detectors. In this paper, we introduce a simple and effective face detector (SEFD). SEFD leverages a computationally light-weight Feature Aggregation Module (FAM) to achieve high computational efficiency of feature fusion and context enhancement. In addition, the aggregation loss is introduced to mitigate the imbalance of the power of feature representation for the classification and regression tasks due to the backbone network initialized by the pre-trained model that focuses on the classification task other than both the regression and classification tasks. SEFD achieves state-of-the-art performance on the UFDD dataset and mAPs of 95.3%, 94.1%, 88.3% and 94.9%, 94.0%, 88.2% on the easy, medium and hard subsets of WIDER Face validation and testing datasets, respectively.
AB - Recently, the state-of-the-art face detectors are extending a backbone network by adding more feature fusion and context extractor layers to localize multi-scale faces. Therefore, they are struggling to balance the computational efficiency and performance of face detectors. In this paper, we introduce a simple and effective face detector (SEFD). SEFD leverages a computationally light-weight Feature Aggregation Module (FAM) to achieve high computational efficiency of feature fusion and context enhancement. In addition, the aggregation loss is introduced to mitigate the imbalance of the power of feature representation for the classification and regression tasks due to the backbone network initialized by the pre-trained model that focuses on the classification task other than both the regression and classification tasks. SEFD achieves state-of-the-art performance on the UFDD dataset and mAPs of 95.3%, 94.1%, 88.3% and 94.9%, 94.0%, 88.2% on the easy, medium and hard subsets of WIDER Face validation and testing datasets, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85081059836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081059836&partnerID=8YFLogxK
U2 - 10.1109/ICB45273.2019.8987231
DO - 10.1109/ICB45273.2019.8987231
M3 - Conference contribution
AN - SCOPUS:85081059836
T3 - 2019 International Conference on Biometrics, ICB 2019
BT - 2019 International Conference on Biometrics, ICB 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 June 2019 through 7 June 2019
ER -