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

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Presentation on theme: "Statistical Learning of Multi-View Face Detection Microsoft Research Asia Stan Li, Long Zhu, Zhen Qiu Zhang, Andrew Blake, Hong Jiang Zhang, Harry Shum."— 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 Non-Object Object Classifier  Evaluate windows at all locations in many scales

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 VerticalHorizontalOn-Off-OnDiagonal

6 Integral Image I( x 1, y 1 ) I( x 3, y 3 ) I( x 2, y 2 ) I( x 4, y 4 ) I( x 6, y 6 )I( x 5, y 5 ) I( x 7, y 7 )I( x 8, y 8 ) y = I 8 – I 7 – I 6 + I 5 + I 4 – I 3 – I 2 + I 1

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

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 AdaBoost Pros 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 Detection Head Rotations In-Plane Rotations: -45 to 45 degrees Out of Plane Rotation: -90 to 90 degreesModerate Nodding

16 Multi-View Face Detection Detector Pyramid

17 Multi-View Face Detection Merging Results FrontalRight SideLeft Side

18 Multi-View Face Detection Summary  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)  20x20 images  3000 original faces, 6000 total  100,000 non-faces Schneiderman FloatBoost/AdaBoost/RBK FloatBoostFloatBoost 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?


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