1. Session 7: Face Detection (cont.)
John Magee
8 February 2017
Slides courtesy of Diane H. Theriault

2. Question of the Day:
How can we find faces in images?

3. Face Detection Compute features in the image Apply a classifier
Viola & Jones. “Rapid Object Detection using a Boosted Cascade of Simple Features”

4. What do Faces “Look Like”?
Chosen features are responses of the image to box filters at specific locations in the image

5. Using Features for Classification
Gateway between the signal processing world and the machine learning world
For any candidate image, we will compute the response of the image to some different filters at some different locations
These will be our input to our machine learning algorithm

6. Using Features for Classification
In Machine Learning, a classifier is a thing that can make a decision about whether a piece of data belongs to a particular class (e.g. “is this image a face or not”)
There are different types of classifiers, and one way to find their parameters by looking at some training data (i.e. “supervised learning”)
A “weak learner” is a classifier for which you can find the best parameters you can, but it still does a mediocre job

7. Using Features for Classification
“Weak Learner” Example: Apply a threshold to a feature (the response of the image to a filter)
How to find a threshold?

8. Using Features for Classification
How to find a threshold?
Start with labeled training data
9,832 face images (positive examples)
10,000 non-face images (sub-windows cut from other pictures containing no faces) (negative examples)
Compute some measure of how good a particular threshold his (e.g. “accuracy”), then find the threshold that gives the best result

9. Using Features for Classification
For a particular threshold on a particular feature, compute:
True positives (faces that are identified as faces)
True negatives (non-face patches that are identified as non-faces)
False positives (non-faces identified as faces)
False negatives (faces identified as non-faces)
Accuracy: % correctly classified
Classification Error: 1 - Accuracy
For each feature, choose a threshold that maximizes the accuracy
Classifier Result
Known Classification
positive
True Positive
False Positive
negative
False Negative
True Negative
“Confusion Matrix”

10. Using Features for Classification
How do you know which feature to use?
Try them all and pick the one that gives the best result
Then, choose the next one that does the next best job, emphasizing the misclassified images
Each threshold on a single feature gives mediocre results, but if you combine them in a clever way, you can get good results
(That’s the extremely short version of “boosting”)

11. Classification with Adaboost
An awesome Machine Learning Algorithm!
Training:
Given a pool of “weak learners” and some data,
Create a “boosted classifier” by
choosing a good combination of K weak learners and associated weights
In our case “Train a Weak Learner” = = Choose a feature to use and which threshold to apply

12. Classification with Adaboost
Training:
Initialization: Assign data weights uniformly to each data point
For 1:K
Train all of the “weak learners”
Compute the weighted classification error using weights assigned to each data point
Choose the weak learner with the lowest weighted error
Compute a classifier weight associated with the weak learner, based on the classification error
Adjust the weights for the data points to emphasize misclassified points
(Specifics of how to compute weights in paper)

13. Classification with Adaboost
Use the “boosted classifier” (the weak learners and associated weights we found during training) to label faces
Evaluate each weak learner we chose on the new data point by
computing the response of the image to the filter and
applying the threshold to
obtain a binary result
Make a final decision by computing a weighted sum of the classification results from the weak learners

14. Classification Cascade
Tradeoff between more “complex” classifier that uses more features (computational cost) and accuracy
What is an acceptable error rate?
What is an acceptable computational cost?
Can we have our cake and eat it too?

15. Classification Cascade
Solution: Use a “cascade” of increasingly complex classifiers
Create less complex classifiers with fewer weak learners that achieve high detection rates (maybe with extra false positives)
Evaluate more complex, more picky, classifiers only after the image passes the early classifiers
Train later classifiers in the cascade using only images that pass earlier classifiers

16. To Detect Faces Divide large images into overlapping sub-windows
Apply classifier cascade to each sub-window
Apply to sub-windows of different sizes by scaling the features (using larger box filters)

18. Discussion Questions:
What is the relationship between an image feature and the response of an image to a box filter applied at a particular location?
If you were given a set of labeled images and a filter response for some particular filter, how would you choose a threshold to use?
How would you adjust your procedure for finding the best possible threshold if you wanted to find the best threshold that recognized at least 99% of faces, even if it let through some non-faces (false positives)?
Given an image, what are the steps for labeling it as face or non-face?
What is a classifier cascade and why would you want to use one?