Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade

1. Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade Paul viola and Michael Jones

2. Outline • Motivation and objective • Definition of feature vectors • Asymmetric Adaboosting • Experiments and Results • Conclusions

3. Motivation • Given a set of images find regions in these images which contain instances of faces.

4. Objectives and Applications • Objective • Fast classification • High detection rate • Low false positive • Application domains • Face detection • Database retrieval

5. Case Study: Face Detection • Difficulty in using raw grayscale pixel values • To capture (learn) ad-hoc domain knowledge classifiers for images. • Computation overhead. • Derive another features by applying simple filters to the pixels.

6. Definition of Simple Features Using a 24x24 pixel base detection window, with all the possible combination of horizontal and vertical location and scale of these feature types, the full set of features sums up to 54,864.

7. Example • The motivation behind using rectangular features, as opposed to more expressive steerable filters is due to their extreme computational efficiency.

8. Integral image • Definition: The integral image at location (x,y), is the sum of the pixel values above and to the left of (x,y), inclusive. • Function: Using the following two recurrences, where i(x,y) is the pixel value of original image at the given location and s(x,y) is the cumulative column sum, we can calculate the integral image representation of the image in a single pass.

9. Rapid Evaluation of Features

10. Features

11. Algorithm Overview • Main components: • Cascading • Asymmetric adaboost • Simple, boosted classifiers can reject many of negative subwindows while detecting all positive instances.

13. Limitation of Adaboost • AdaBoost minimizes a quantity related to classification error; it does not minimize the number of false negatives. • Unfortunately feature selection proceeds as if classification error were the only goal, and the features selected are not optimal for the task of rejecting negative examples

14. Asymmetric Adaboost • If we hope to minimize false negatives then the weight on positive examples could be increased so that the minimum error criteria will also have very few false negatives.

15. Asymmetric Adaboost • Minimization of this bound can be achieved using AdaBoost by pre-weighting each example by exp(0.5*yi log k ) • Using the steps followed in Adaboost we obtain

16. Layer 2 5000 faces 10, 000 non-face Weak classifier 1 Weak classifier 3 Weak classifier 3 Which three? Using asymmetric adaboost to obtain three classifier 49999 1 2 3 49998 Training 5000 faces 5000 false positive from layer 1 Layer1 Weak classifier 1 Weak classifier 2 Weak classifier 3 50,000

17. Cascading

18. Experiment • Naive asymmetric v.s. asymmetric boosting

19. Experiment • asymmetric boosting v.s. normal boosting

20. Results

21. Results 18 Layers 22 Layers

22. Result 26 Layers

23. Questions

24. Thank You