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DataScience Lab 2017_Обзор методов детекции лиц на изображение
The document surveys various face detection approaches, detailing prominent datasets such as FDDB, IJB-A, and Wider Face, highlighting their unique features and evaluation methodologies. It discusses specific algorithms like the Viola-Jones object detector and convolutional neural networks, including techniques for improving detection accuracy through cascaded classifiers and hard negative mining. The document aims to provide insights into the challenges and advancements in unconstrained face detection technology.
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DataScience Lab 2017_Обзор методов детекции лиц на изображение
- 1. Survey of FaceDetection Approaches Yurii Pashchenko DataScience Lab, Odessa, 2017
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- 4. Evaluation metric. ReceiverOperating Characteristic (ROC) 4
- 5. Benchmarks ● FDDB ● AFW ●PascalFace ● IJB-A ● MALF ● WIDER Face 5
- 6. FDDB: A Benchmarkfor 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
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- 9. IARPA Janus BenchmarkA (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. IJB-A. Evaluation * FalseAccept and Detection Rate are computed per image 10
- 11. WIDER FACE: AFace 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
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- 13. WIDER FACE. Evaluationresults 13
- 14. Comparison of FaceDetection Datasets https://arxiv.org/pdf/1511.06523.pdf 14
- 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. Main Principles ● Scanningwindow ● 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
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- 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. Feature selection ⚫Idea: Combiningseveral 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. Cascaded Classifier ● A1 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
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- 23. Viola Jones. EvaluationResults on FDDB 23
- 24. A Convolutional NeuralNetwork 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. Cascade CNN. CalibrationNet 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. Cascade CNN. EvaluationResults 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. Joint Face Detectionand 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. MTCNN. Evaluation onFDDB and WIDER https://arxiv.org/pdf/1604.02878.pdf 28
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- 31. Bootstrapping Face Detectionwith 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. Face Detection usingDeep 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. DeepIR. Evaluation onFDDB DeepIR https://arxiv.org/pdf/1701.08289.pdf 33
- 34. Finding Tiny Faces(HR-ER) https://arxiv.org/pdf/1612.04402.pdf 34
- 35. HR-ER. Approach What aboutcontext? https://arxiv.org/pdf/1612.04402.pdf 35
- 36. HR-ER. Evaluation onWIDER and FDDB https://arxiv.org/pdf/1612.04402.pdf 36
- 37. THANK YOU FORYOUR ATTENTION! e-mail: yurii.pashchenko@ring.com skype: george.pashchenko 37