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.
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Acknowledgment
This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61573068 and 61871052.
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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
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DOI: https://doi.org/10.1007/978-3-030-31456-9_23
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