Review and testing of frontal face detectors
I.A. Kalinovskii, V.G. Spitsyn

 

Tomsk Polytechnic University

Full text of article: Russian language.

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Abstract:
This paper presents comparison results for the proposed face detection algorithm based on a compact convolutional neural network cascade and modern frontal face detectors. Test results for 16 frontal view face detectors on two public benchmarks datasets are shown. A comparative assessment of the performance of face detection algorithms is made.

Keywords:
face detection, cascade classifiers, convolutional neural networks, deep learning.

Citation:
Kalinovskii IA, Spitsyn VG. Review and testing of frontal face detectors. Computer Optics 2016; 40(1): 99-111. DOI: 10.18287/2412-6179-2016-40-1-99-111.

References:

  1. Kalinovskii IA, Spitsyn VG. Compact Convolutional Neural Network Cascade for Face Detection. Source: <http://arxiv.org/abs/1508.01292.pdf>.
  2. Viola P, Jones MJ. Rapid object detection using a boosted cascade of simple features. IEEE Conference on Computer Vision and Pattern Recognition; 2001; 1: 511-518.
  3. Lienhart R, Maydt J. An extended set of Haar-like features for rapid object detection. IEEE International Conference on Image Processing; 2002: 1: 900–903.
  4. Jain V, Learned-Miller E. Online domain adaptation of a pre-trained cascade of classifiers. IEEE Conference on Computer Vision and Pattern Recognition; 2011; 577–584.
  5. Subburaman V, Marcel S. Fast bounding box estimation based face detection. European Conference on Computer Vision, Workshop on Face Detection; 2010; 1–14.
  6. Markuš N, Frljak M, Pandzic IS, Ahlberg J, Forchheimer R. A method for object detection based on pixel intensity comparisons organized in decision trees. Source: < http://arxiv.org/abs/1305.4537.pdf>.
  7. Li J, Zhang Y. Learning SURF cascade for fast and accurate object detection. IEEE Conference on Computer Vision and Pattern Recognition; 2013; 3468–3475.
  8. Barr JR, Bowyer KW, Flynn PJ. The effectiveness of face detection algorithms in unconstrained crowd scenes. IEEE Winter Conference on Applications of Computer Vision; 2014; 1020–1027.
  9. Yang B, Yan J, Lei Z, Li SZ. Aggregate channel features for multi-view face detection. IEEE International Joint Conference on Biometrics; 2014; 1-8.
  10. Mathias M, Benenson R, Pedersoli M, Van Gool L. Face detection without bells and whistles. European Conference on Computer Vision; 2014; 720-735.
  11. Zhang C, Zhang Z. Improving multiview face detection with multi-task deep convolutional neural networks. IEEE Winter Conference on Applications of Computer Vision; 2014; 1036-1041.
  12. Chen D, Ren S, Wei Y, Cao X, Sun J. Joint cascade face detection and alignment. European Conference on Computer Vision; 2014; 109-122.
  13. Zhu X, Ramanan D. Face detection, pose estimation, and landmark localization in the wild. IEEE Conference on Computer Vision and Pattern Recognition; 2012; 2879-2886.
  14. Li H, Lin Z, Brandt J, Shen X, Hua G. Efficient boosted exemplar-based face detection. IEEE Conference on Computer Vision and Pattern Recognition; 2014; 1843-1850.
  15. Zeiler M, Fergus R. Visualizing and understanding convolutional networks. European Conference on Computer Vision; 2014; 818-833.
  16. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. Source: <http://arxiv.org/abs/1409.4842.pdf>.
  17. Garcia C, Delakis M. Convolutional face finder: A neural architecture for fast and robust face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence; 2004; 1408-1423.
  18. Osadchy M, LeCun Y, Miller M. Synergistic face detection and pose estimation with energy-based models. Journal of Machine Learning Research; 2007; 1197-1215.
  19. Farfade SS, Saberian M, Li L-J. Multi-view face detection using deep convolutional neural networks. International Conference on Multimedia Retrieval; 2015.
  20. Li H, Lin Z, Shen X, Brandt J, Hua G. A Convolutional neural network cascade for face detection. IEEE Conference on Computer Vision and Pattern Recognition; 2015; 5325-5334.
  21. Kalinovskii IA, Spitsyn VG. Algorithm for face detection on Ultra HD video [In Russian]. Conference on technical vision in control systems; 2015; 95-96.
  22. Ko?stinger M, Wohlhart P, Roth PM, Bischof H. Annotated Facial Landmarks in the Wild: A Large-scale, real-world database for facial landmark localization. IEEE International Conference on Computer Vision Workshops; 2011; 2144-2151.
  23. Vasilache N, Johnson J, Mathieu M, Chintala S, Piantino S, LeCun Y. Fast convolutional nets with fbfft: A GPU performance evaluation. Source: <http://arxiv.org/abs/ 1412.7580.pdf>.
  24. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. Source: <http://arxiv.org/abs/1502.03167.pdf>.
  25. Lee C-Y, Xie S, Gallagher P, Zhang Z, Tu Z. Deeply-supervised nets. Source: <http://arxiv.org/abs/ 1409.5185.pdf>.
  26. Wolf L, Hassner T, Maoz I. Face recognition in unconstrained videos with matched background similarity. IEEE Conference on Computer Vision and Pattern Recognition; 2014; 529-534.
  27. Kalinovskii IA, Spitsyn VG. Face detection algorithm based on the convolutional neural network [In Russian]. Neurocomputers: Development and Applications; 2013; 10: 48-53.
  28. Le QV, Coates A, Prochnow B, Ng AY. On Optimization Methods for Deep Learning. International Conference on Machine Learning; 2011; 265-272.
  29. Ko?stinger M. Efficient metric learning for real-world face recognition. Graz University of Technology. PhD thesis; 2013.
  30. Pham MT, Cham TJ. Fast training and selection and Haar features using statistics in boosting-based face detection. IEEE International Conference on Computer Vision; 2007; 1-7.
  31. Kienzle W, Bakir G, Franz M, Scholkopf B. Face detection: efficient and rank deficient. Advances in Neural Information Processing Systems; 2005; 673-680.
  32. Jain V, Learned-Miller E. FDDB: A Benchmark for face detection in unconstrained settings. Technical Report UM-CS-2010-009. University of Massachusetts; 2010.
  33. Yang B, Yan J, Lei Z, Li SZ. Fine-grained evaluation on face detection in the wild. IEEE International Conference on Automatic Face and Gesture Recognition; 2015.
  34. Klare BF, Klein B, Taborsky E, Blanton A, Cheney J, Allen K, Grother P, Mah A, Burge M, Jain AK. Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A. IEEE Conference on Computer Vision and Pattern Recognition; 2015; 1931-1939.
  35. Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves // International Conference on Machine Learning; 2006; 233-240.
  36. Everingham M, Gool LV, Williams C, Winn J, Zisserman A. The PASCAL visual object classes (VOC) challenge. International Journal of Computer Vision; 2010; 88(2): 303-338.
  37. Oro D, Fernandez C, Saeta JR, Martorell X, Hernando J. Real-time GPU-based face detection in HD video sequences. IEEE International Conference Computer Vision Workshops; 2011; 530–537.
  38. Nguyen T, Hefenbrock D, Oberg J, Kastner R, Baden S. A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs. Journal of Parallel and Distributed Computing; 2013; 73(5): 677–685.
  39. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y. OverFeat: Integrated recognition, localization and detection using convolutional networks. Source: <http://arxiv.org/abs/1312.6229.pdf>.
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