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Single-shot face and landmarks detector
Y.V. Vizilter 1, V.S. Gorbatsevich 1, A.S. Moiseenko 1,2

State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia,
Moscow Institute of Physics and Technology (MIPT), Moscow, Russia

 PDF, 2028 kB

DOI: 10.18287/2412-6179-CO-674

Pages: 589-595.

Full text of article: Russian language.

Abstract:
Facial landmark detection is an important sub-task in solving a number of biometric facial recognition tasks. In face recognition systems, the construction of a biometric template occurs according to a previously aligned (normalized) face image and the normalization stage includes the task of finding facial keypoints. A balance between quality and speed of the facial keypoints detector is important in such a problem. This article proposes a CNN-based one-stage detector of faces and keypoints operating in real time and achieving high quality on a number of well-known test datasets (such as AFLW2000, COFW, Menpo2D). The proposed face and facial landmarks detector is based on the idea of a one-stage SSD object detector, which has established itself as an algorithm that provides high speed and high quality in object detection task. As a basic CNN architecture, we used the ShuffleNet V2 network. An important feature of the proposed algorithm is that the face and facial keypoint detection is done in one CNN forward pass, which can significantly save time at the implementation stage. Also, such multitasking allows one to reduce the percentage of errors in the facial keypoints detection task, which positively affects the final face recognition algorithm quality.

Keywords:
biometry, face detection, CNN, landmarks detection, SSD.

Citation:
Vizilter YV, Gorbatsevich VS, Moiseenko AS. Single-shot face and landmarks detector. Computer Optics 2020; 44(4): 589-595. DOI: 10.18287/2412-6179-CO-674.

Acknowledgements:
This work was financially supported by the Russian Foundation for Basic Research (Project 19-07-01146 А).

References:

  1. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC. SSD: Single shot multibox detector. ECCV 2016: 21-37.
  2. Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R. A survey of deep learning-based object detection. IEEE Access 2019; 7: 128837-128868.
  3. Deng J, Guo J, Xue N, Zafeiriou S. ArcFace: Additive angular margin loss for deep face recognition. CVPR 2019: 4685-4694.
  4. Liu W, Wen Y, Yu Z, Li M, Raj B, Song L. SphereFace: Deep hypersphere embedding for face recognition. CVPR 2017: 6738-6746.
  5. Zhang K, Zhang Z, Li Z, Qiao Y. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 2016; 23(10): 1499-1503.
  6. Deng J, Guo J, Zhou Y, Yu J, Kotsia I, Zafeiriou S. RetinaFace: Single-stage dense face localisation in the wild. Source: <https://arxiv.org/abs/1905.00641>.
  7. Shen Z, Liu Z, Li J, Jiang Y-G, Chen Y, Xue X. DSOD: Learning deeply supervised object detectors from scratch. ICCV 2017: 1937-1945.
  8. Lin T-Y, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. ICCV 2017: 2999-3007.
  9. Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR 2014: 580-587.
  10. Girshick R. Fast R-CNN. ICCV 2015: 1440-1448.
  11. 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 2015; 39(6): 1137-1149.
  12. Lin Y, Dollar P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. CVPR 2017. DOI: 10.1109/CVPR.2017.106.
  13. Ma N, Zhang X, Zheng H-T, Sun J. ShuffleNet V2: Practical guidelines for efficient CNN architecture design. ECCV 2018: 122-138.
  14. Zhang S, Zhu X, Lei Z, Shi H, Wang X, Li SZ. S3FD: Single shot scale-invariant face detector. ICCV 2017: 192-201.

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