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DataScience Lab 2017_Обзор методов детекции лиц на изображение

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DataScience Lab, 13 мая 2017
Обзор методов детекции лиц на изображение
Юрий Пащенко ( Research Engineer, Ring Labs)
В данном докладе мы предлагаем обзор наиболее новых и популярных методов обнаружения лиц, таких как Viola-Jones, Faster-RCNN, MTCCN и прочих. Мы обсудим основные критерии оценки качества алгоритма а также базы, включая FDDB, WIDER, IJB-A.
Все материалы: http://datascience.in.ua/report2017

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DataScience Lab 2017_Обзор методов детекции лиц на изображение

  1. 1. Survey of Face Detection Approaches Yurii Pashchenko DataScience Lab, Odessa, 2017
  2. 2. Classification vs. Detection http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf 2
  3. 3. Evaluation 3
  4. 4. Evaluation metric. Receiver Operating Characteristic (ROC) 4
  5. 5. Benchmarks ● FDDB ● AFW ● PascalFace ● IJB-A ● MALF ● WIDER Face 5
  6. 6. FDDB: A Benchmark for Face Detection in Unconstrained Settings ● 2 845 images with a total of 5 171 faces; ● a wide range of difficulties: ○ occlusions ○ different poses ○ low resolution ○ out-of-focus faces ● the specification of face regions as elliptical regions ● both grayscale and color images. http://vis-www.cs.umass.edu/fddb/ 6
  7. 7. FDDB. Annotation http://vis-www.cs.umass.edu/fddb/fddb.pdf 7
  8. 8. FDDB.Evaluation 8
  9. 9. IARPA Janus Benchmark A (IJB-A) • 5 712 images and 2085 videos, with an average of 11.4 images and 4.2 videos per subject • full pose variation • joint use for face recognition and face detection benchmarking • a mix of images and videos • wider geographic variation of subjects • landmark locations Brendan F Klare, Emma Taborsky, Austin Blanton, Jordan Cheney, Kristen Allen, Patrick Grother, Alan Mah, Mark Burge, and Anil K Jain. 2015. Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 1931–1939 9
  10. 10. IJB-A. Evaluation * False Accept and Detection Rate are computed per image 10
  11. 11. WIDER FACE: A Face Detection Benchmark • It consists of 32 203 images with 393 703 labeled faces, which is 10 times larger than the current largest face detection dataset • The faces vary largely in appearance, pose, and scale • Annotated multiple attributes: occlusion, pose, and event categories, which allows in depth analysis of existing algorithms. http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/ 11
  12. 12. WIDER FACE. Annotations https://arxiv.org/pdf/1511.06523.pdf 12
  13. 13. WIDER FACE. Evaluation results 13
  14. 14. Comparison of Face Detection Datasets https://arxiv.org/pdf/1511.06523.pdf 14
  15. 15. Viola-Jones Object Detector • Very popular for Human Face Detection • May be trained for Cat and Dog Face detection • Available free in OpenCV library (http://opencv.org) O. Parkhi, A. Vedaldi, C. V. Jawahar, and A. Zisserman. The Truth about Cats and Dogs // Proceedings of the International Conference on Computer Vision (ICCV), 2011. J. Liu, A. Kanazawa, D. Jacobs, P. Belhumeur. Dog Breed Classification Using Part Localization // Lecture Notes in Computer Science Volume 7572, 2012, pp 172-185.
  16. 16. Main Principles ● Scanning window ● Features ● Integral image ● Boosted feature selection ● Cascaded classifier P.A. Viola, M.J. Jones, Rapid object detection using a boosted cascade of simple features, in: CVPR, issue 1, 2001, pp. 511–518. 16
  17. 17. Scaning window 17
  18. 18. Integral Image 18
  19. 19. Features ⚫Available features: ⚫ HAAR ⚫ LBP ⚫ HOG ⚫Too many features! ⚫ location, scale, type ⚫ 180,000+ possible features associated with each 24 x 24 window ⚫Not all of them are useful! 19
  20. 20. Feature selection ⚫Idea: Combining several weak classifiers to generate a strong classifier α1 α2 α3 αT … … α1 h1 + α2 h2 + α3 h3 + … + αT hT > < Tthreshol d weak classifier (feature, threshold) h1 = 1 or 0 20
  21. 21. Cascaded Classifier ● A 1 feature classifier achieves 100% detection rate and about 50% false positive rate. ● A 5 feature classifier achieves 100% detection rate and 40% false positive rate (20% cumulative) – using data from previous stage. ● A 20 feature classifier achieve 100% detection rate with 10% false positive rate (2% cumulative) 21
  22. 22. Viola Jones Pipeline https://habrahabr.ru/post/133826/ 22
  23. 23. Viola Jones. Evaluation Results on FDDB 23
  24. 24. A Convolutional Neural Network Cascade for Face Detection ● 12-net ● 12-calibration-net ● 24-net ● 24-calibration-net ● 48-net ● 48-calibration-net http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Li_A_Convolutional_Neural_2015_CVPR_paper.pdf 24
  25. 25. Cascade CNN. Calibration Net The calibration pattern adjusts the window to be N = 45 patterns, formed by all combinations of http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Li_A_Convolutional_Neural_2015_CVPR_paper.pdf 25
  26. 26. Cascade CNN. Evaluation Results on FDDB ~14 fps on CPU ~100 fps on GPU http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Li_A_Convolutional_Neural_2015_CVPR_paper.pdf 26
  27. 27. Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (MTCCN) • Improved previous approach • Joint face detection and alignment • Online Hard sample mining • Multi-source training https://arxiv.org/pdf/1604.02878.pdf 27
  28. 28. MTCNN. Evaluation on FDDB and WIDER https://arxiv.org/pdf/1604.02878.pdf 28
  29. 29. Faster R-CNN 29
  30. 30. Region proposal network 30
  31. 31. Bootstrapping Face Detection with Hard Negative Examples • ResNet-50 • Foreground ROI thr >=0.5 • Background ROI in the interval [0.1, 0.5) • Balancing bg-fg RoIs: 3:1 • Hard Negative mining https://arxiv.org/pdf/1608.02236.pdf 31
  32. 32. Face Detection using Deep Learning: An Improved Faster RCNN Approach (DeepIR) • VGG16 architecture • Hard negative mining • Feature concatenation • Multi-scale training https://arxiv.org/pdf/1701.08289.pdf 32
  33. 33. DeepIR. Evaluation on FDDB DeepIR https://arxiv.org/pdf/1701.08289.pdf 33
  34. 34. Finding Tiny Faces (HR-ER) https://arxiv.org/pdf/1612.04402.pdf 34
  35. 35. HR-ER. Approach What about context? https://arxiv.org/pdf/1612.04402.pdf 35
  36. 36. HR-ER. Evaluation on WIDER and FDDB https://arxiv.org/pdf/1612.04402.pdf 36
  37. 37. THANK YOU FOR YOUR ATTENTION! e-mail: yurii.pashchenko@ring.com skype: george.pashchenko 37

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