1 / 25

Statistical Learning of Multi-View Face Detection

Statistical Learning of Multi-View Face Detection. Microsoft Research Asia Stan Li, Long Zhu, Zhen Qiu Zhang, Andrew Blake, Hong Jiang Zhang, Harry Shum Presented by Derek Hoiem. Overview. Viola-Jones AdaBoost FloatBoost Approach Multi-View Face Detection FloatBoost Results

ivan-cunningham
Download Presentation

Statistical Learning of Multi-View Face Detection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Statistical Learning of Multi-View Face Detection Microsoft Research Asia Stan Li, Long Zhu, Zhen Qiu Zhang, Andrew Blake, Hong Jiang Zhang, Harry Shum Presented by Derek Hoiem

  2. Overview • Viola-Jones AdaBoost • FloatBoost Approach • Multi-View Face Detection • FloatBoost Results • FloatBoost vs. AdaBoost • FloatBoost Discussion

  3. Face Detection Overview • Evaluate windows at all locations in many scales Classifier Non-Object Object

  4. Viola-Jones AdaBoost • Weak classifiers formed out of simple features • In sequential stages, features are selected and weak classifiers trained with emphasis on misclassified examples • Integral images and a cascaded classifier allow real-time face detection

  5. Viola-Jones Features • For a 24 x 24 image: 190,800 semi-continuous features • Computed in constant time using integral image • Weak classifiers consist of filter response threshold Vertical Horizontal On-Off-On Diagonal

  6. Integral Image y = I8 – I7– I6 + I5+ I4 – I3 – I2 + I1 I( x1, y1 ) I( x2, y2 ) I( x4, y4 ) I( x3, y3 ) I( x5, y5 ) I( x6, y6 ) I( x7, y7 ) I( x8, y8 )

  7. Cascade of Classifiers Input Signal (Image Window) 40% Stage 1 1 Weak Classifier 60% 40% Stage 2 5 Weak Classifiers Class 2 (Non-Face) 60% 99.999% … 0.001% 40% Stage N 1200 Weak Classifiers Class 1 (Face)

  8. Viola-Jones AdaBoost Algorithm • Strong classifier formed from weak classifiers: • At each stage, new weak classifier chosen to minimize bound on classification error (confidence weighted): • This gives the form for our weak classifier:

  9. Viola-Jones AdaBoost Algorithm

  10. Viola-Jones AdaBoostPros and Cons • Very fast • Moderately high accuracy • Simple implementation/concept • Greedy search through feature space • Highly constrained features • Very high training time

  11. FloatBoost • Weak classifiers formed out of simple features • In each stage, the weak classifier that reduces error most is added • In each stage, if any previously added classifier contributes to error reduction less than the latest addition, this classifier is removed • Result is a smaller feature set with same classification accuracy

  12. MS FloatBoost Features • For a 20 x 20 image: over 290,000 features (~500K ?) • Computed in constant time using integral image • Weak classifiers consist of filter response threshold Microsoft Viola-Jones

  13. FloatBoost Algorithm

  14. FloatBoost Weak Classifiers • Can be portrayed as density estimation on single variables using average shifted histograms with weighted examples • Each weak classifier is a 2-bin histogram from weighted examples • Weights serve to eliminate overcounting due to dependent variables • Strong classifier is a combination of estimated weighted PDFs for selected features

  15. Multi-View Face DetectionHead Rotations In-Plane Rotations: -45 to 45 degrees Out of Plane Rotation: -90 to 90 degrees Moderate Nodding

  16. Multi-View Face DetectionDetector Pyramid

  17. Multi-View Face DetectionMerging Results Frontal Right Side Left Side

  18. Multi-View Face DetectionSummary • Simple, rectangular features used • FloatBoost selects and trains weak classifiers • A cascade of strong classifiers makes up the overall detector • A coarse-to-fine evaluation is used to efficiently find a broad range of out-of-plane rotated faces

  19. Results: Frontal (MIT+CMU) FloatBoost/AdaBoost/RBK • 20x20 images • 3000 original faces, 6000 total • 100,000 non-faces Schneiderman FloatBoost FloatBoost vs. Adaboost

  20. Results: MS Adaboost vs. Viola-Jones Adaboost • More flexible features • Confidence-weighted AdaBoost • Smaller image size

  21. Results: Profile No Quantitative Results!!!

  22. FloatBoost vs. AdaBoost • FloatBoost finds a more potent set of weak classifiers through a less greedy search • FloatBoost results in a faster, more accurate classifier • FloatBoost requires longer training times (5 times longer)

  23. FloatBoost vs. AdaBoost 1 Strong Classifier, 4000 objects, 4000 non-objects, 99.5% fixed detection

  24. FloatBoost: Pros • Very Fast Detection (5 fps multi-view) • Fairly High Accuracy • Simple Implementation

  25. FloatBoost: Cons • Very long training time • Not highest accuracy • Does it work well for non-frontal faces and other objects?

More Related