Face Recognition Pipeline 1

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1 Face Detection

2 Face Recognition Pipeline 1 Face recognition is a visual pattern recognition problem A face recognition system generally consists of four modules as depicted below 1 S.Li and A.Jain, (ed). Handbook of Face Recognition. Springer-Verlag, 2005

3 Face Detection OpenCV face detection Uses a method that Paul Viola and Michael Jones published in May contain improvements from Lienhart and Maydt (2002) 3 Usually called simply the Viola-Jones method, or even just Viola-Jones This approach to detecting objects combines four key concepts: o Simple rectangular features, called Haar-like features o Integral image for rapid feature detection o AdaBoost machine-learming method o Cascaded classifier to combine many features efficiently 2 P. Viola and M.J. Jones", Rapid Object Detection using a Boosted Cascade of Simple Features," IEEE CVPR R. Lienhart and J. Maydt. An Extended Set of Haar-like Features for Rapid Object Detection. IEEE ICIP 2002

Face Detection: Haar-like Features 2 Four basic types. They are easy to calculate. The sum of the white areas are subtracted from the sum of the black ones.

4 Face Detection: Haar-like Features 2 Four basic types. They are easy to calculate. The sum of the white areas are subtracted from the sum of the black ones. A special representation of the sample called the integral image makes feature extraction faster. 2 P. Viola and M.J. Jones", Rapid Object Detection using a Boosted Cascade of Simple Features," IEEE CVPR 2001

5 Face Detection: Integral Image The integral image at location x, y contains the sum of the pixels above and to the left of x, y inclusive II x, y = i(x, y) x x, y y x x, y y Original Image i(x, y) II x, y Integral Image

6 Face Detection: Integral Image A B 1 2 C D 3 4 Integral Image What is the sum of the pixels within rectangle D, using the four array positions, 1, 2, 3 and 4? (2 + 3)

7 Face Detection: Haar-like Features 3 3 R. Lienhart and J. Maydt. An Extended Set of Haar-like Features for Rapid Object Detection. IEEE ICIP 2002

Face Detection: Haar-like Features 3 (24) (24) X = floor(w/w) Y = floor(h/h) Upright # of features XY W + 1 w X + 1 2 H + 1 h Y + 1 2 Number of features

8 Face Detection: Haar-like Features 3 (24) (24) X = floor(w/w) Y = floor(h/h) Upright # of features XY W + 1 w X H + 1 h Y Number of features inside of a window for each prototype 3 R. Lienhart and J. Maydt. An Extended Set of Haar-like Features for Rapid Object Detection. IEEE ICIP 2002

Face Detection: Haar-like Features Demo OpenCV (Open Source Computer Vision Library) - is a library of programming functions mainly aimed at real time computer vision Developed by Intel

The goals of the project were described as: Advance vision research by providing not only open but also optimized code for basic vision infrastructure. No more reinventing the wheel.

9 Face Detection: Haar-like Features Demo OpenCV (Open Source Computer Vision Library) - is a library of programming functions mainly aimed at real time computer vision Developed by Intel and now supported by Willow Garage. The goals of the project were described as: Advance vision research by providing not only open but also optimized code for basic vision infrastructure. No more reinventing the wheel. Disseminate vision knowledge by providing a common infrastructure that developers could build on, so that code would be more readily readable and transferable. Advance vision-based commercial applications by making portable, performanceoptimized code available for free with a license that did not require to be open or free themselves.

10 Face Detection: Haar-like Features Demo OpenCV Face Detection Examples

Face Detection: Haar-like Features Demo http://www.lienhart.de/source_code/source_code.html 3 3 R.

11 Face Detection: Haar-like Features Demo R. Lienhart and J. Maydt. An Extended Set of Haar-like Features for Rapid Object Detection. IEEE ICIP 2002

12 Face Detection: Haar-like Features Demo

13 Face Detection: Haar-like Features Demo Readme file mex setup mexme_fdt.m demo_haar.m viola_24x24.mat haar_dico_2.mat haar_dico_4.mat haar_dico_5.mat haar_dico_19.mat

14 Face Detection: Haar-like Features Demo rect_param Features rectangles parameters (10 x nr), where nr is the total number of rectangles for the patterns. (default Vertical(2 x 1) [1 ; -1] and Horizontal(1 x 2) [-1, 1] patterns) rect_param(:, i) = [ip ; wp ; hp ; nrip ; nr ; xr ; yr ; wr ; hr ; sr], where ip wp hp nrip nr xr,yr wr,hr sr index of the current pattern. ip = [1,...,nP], where np is the total number of patterns width of the current pattern height of the current pattern total number of rectangles for the current pattern ip index of the current rectangle of the current pattern, nr=[1,...,nrip] top-left coordinates of the current rectangle of the current pattern width and height of the current rectangle of the current pattern weights of the current rectangle of the current pattern

15 Face Detection: Haar-like Features Demo Debugging MEX files

16 Face Detection: AdaBoost Machine-Learning Method Bagging - procedure for combining different classifiers constructed using the same data set - acronym for bootstrap aggregating - motivation of combining classifiers is to improve an unstable classifier - an unstable classifier is one where a small change in the learning set/classification parameters produces a large change in the classifier

17 Face Detection: AdaBoost Machine-Learning Method Procedure Bagging 1. Assume we have a set of n observations with class labels, w j 2. Generate a bootstrap sample of size n by sampling with replacement from the original data set 3. Construct a classifier using the bootstrap sample in step 2 4. Repeat steps 2 and 3, B times. This yields B classifiers 5. To classify a new observation x, assign a class label to the observation using each B classifiers. This yields B class labels for x. Assign the class label to x that occurs with the highest frequency among the B class labels. Ties are broken arbitrarily.

18 Face Detection: AdaBoost Machine-Learning Method Procedure Bagging Example Computational Statistics Handbook with MATLAB, 2nd Edition Wendy L. and Angel R. Martinez insect data three variables measured on ten insects from each of three species (Hand, et al., 1994)

19 Face Detection: AdaBoost Machine-Learning Method Boosting procedure for combining weak/unstable classifiers in order to boost or improve performance a weak classifier is one where the performance is just slightly better than random main idea is to first assign equal weights to each observation in the data set a classifier is constructed using the weighted points, and the classification errors are assessed with the training set at the next iteration, misclassified data points are given higher weights, increasing their importance

20 Face Detection: AdaBoost Machine-Learning Method

21 Face Detection: AdaBoost Machine-Learning Method Boosting Example

22 Face Detection: AdaBoost Machine-Learning Method First classifier

23 Face Detection: AdaBoost Machine-Learning Method First 2 classifiers

24 Face Detection: AdaBoost Machine-Learning Method First 3 classifiers

25 Face Detection: AdaBoost Machine-Learning Method Final Classifier learned by Boosting

26 Face Detection: AdaBoost Machine-Learning Method

27 References

Face Detection on OpenCV using Raspberry Pi

Face Detection on OpenCV using Raspberry Pi Narayan V. Naik Aadhrasa Venunadan Kumara K R Department of ECE Department of ECE Department of ECE GSIT, Karwar, Karnataka GSIT, Karwar, Karnataka GSIT, Karwar,