Abstract
Face detection is the precondition of various research fields, involving face recognition, face identification, face expression analysis, etc. The existing object detection methods, whether two-stage methods or one-stage ones, expect to balance speed and accuracy. Meanwhile, YOLOv3, as a popular object detection algorithm, has gained obvious advantages in both speed and accuracy. Thus, we migrated YOLOv3 to the face detection area and made some improvements to adjust it to the face detection problem, including changing the detection layer to detect smaller faces, choosing the Softmax as the loss function instead of the logistic classifier to maximize the difference of inter-class features, and decreasing the dimension of features on detection layers to improve the speed. This method was trained on the WIDER FACE database and the CelebA database and tested on the FDDB database. Experimental results have shown that the face detection method based on YOLOv3 has obtained great performance on small face, speed, and accuracy for the face detection task.
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References
Viola, P., Way, O.M., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)
P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. Comput. Vision Pattern Recogn. (CVPR), 511–518 (2001)
Forsyth, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Computer Society (2014)
Girshick, R.: Fast R-CNN. Comput. Sci. (2015)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks (2015)
Najibi, M., Samangouei, P., Chellappa, R., et al.: SSH: Single stage headless face detector. In: IEEE International Conference on Computer Vision, pp. 4885–4894. IEEE Computer Society (2017)
Liu, W., Anguelov, D., Erhan, D., et al.: SSD: Single Shot MultiBox Detector. In: European Conference on Computer Vision, pp. 21–37. Springer, Cham (2016)
Li, H., Lin, Z., Shen, X., et al.: A convolutional neural network cascade for face detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society (2015)
Zhang, K., Zhang, Z., Li, Z., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection (2015)
Everingham, M., Gool, L.V., Williams, C.K.I., et al.: The Pascal Visual Object Classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525 (2017)
Redmon J, Farhadi A.: YOLOv3: an incremental improvement (2018)
Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection (2016)
Acknowledgements
Our paper is supported by the National Key Research and Development Plan (Grant No. 2016YFC0801005) and the Basic Research Fund Project of People’s Public Security University of China (Grant No. 2018JKF617).
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Li, C., Wang, R., Li, J., Fei, L. (2020). Face Detection Based on YOLOv3. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_34
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DOI: https://doi.org/10.1007/978-981-13-9406-5_34
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