Abstract
In the latest field of object detection, whatever it is one-stage approach or two-stage approach, both of them are using the CNNs with complex calculations to guide the detection performance better. But this also greatly limits our use of the platform (only available on the GPU), so we propose a simple light-weight network for face detection based on the well performance light-weight network backbone which can run on CPU or ARM. In our approach, we have a light-weight network that combine a simple but effective detection framework, a hyperparameter to control the number of channels. It makes our model allowed to have smaller model, faster speed and better accuracy. LWFD can perform CNN inference on mobile devices, and 1.0x run at 90 ms on 2.4 GHz CPU with f-score of 89% on FDDB dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Szegedy, C., et al.: Going deeper with convolutions (2014)
He, K., Zhang, X., Ren, S., Jian, S.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)
Chollet, F.: Xception: deep learning with depthwise separable convolutions (2016)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)0.5mb model size (2016)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks (2018)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017)
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices (2017)
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design (2018)
Wang, M., Deng, W.: Deep face recognition: a survey. CoRR abs/1804.06655 (2018)
Li, S., Deng, W.: Deep facial expression recognition: a survey. CoRR abs/1804.08348 (2018)
Li, Z., Chao, P., Gang, Y., Zhang, X., Jian, S.: Light-head R-CNN: in defense of two-stage object detector (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)
Li, H., Zhe, L., Shen, X., Brandt, J., Gang, H.: A convolutional neural network cascade for face detection. In: Computer Vision and Pattern Recognition (2015)
Zhang, K., Zhang, Z., Li, Z., Yu, Q.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016)
Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: UnitBox: an advanced object detection network (2016)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017)
Acknowledgment
This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61573068 and 61871052.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liang, H., Hu, J., Deng, W. (2019). LWFD: A Simple Light-Weight Network for Face Detection. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-31456-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31455-2
Online ISBN: 978-3-030-31456-9
eBook Packages: Computer ScienceComputer Science (R0)