This document discusses face detection and recognition techniques. It introduces the problems of detecting where a face is located in an image (face detection) and identifying who the face belongs to (face recognition). It then describes Viola and Jones' approach which uses AdaBoost learning on Haar-like features computed quickly using integral images to build a classifier cascade that can discard non-face regions and focus on potential face areas. Key steps involve using integral images and Haar-like features for fast computation, AdaBoost for feature selection, and a classifier cascade for efficient scanning.

1. Face
Detection
Pattern Recognition Part

2. Problem Definition & History
Face Detection
 Where is the face?
Face Recognition
 Who is this?

3. Face Recognition Attempts

4. Face Recognition Attempts

5. Voila-Jones Approach
AI section
 Learning based on
AdaBoost
 Classifier Cascade
PR section
 Integral Image
 Haar-Like Features

6. Integral Image
 New image representation which allows
the features used by our detector to be
computed very quickly.

7. Haar-Like Features
 New method to detect the features of a
face in an Image

8. Classifier Cascade
 Which allows background region of the
image to be discarded quickly, while
spending more computations on
promising the object-like region

9. Adaptive-Boost
 A learning Algorithm, which selects a small
number of critical visual features from a
large set.

10. Part II
Pattern Recognition

11. Integral Image

12. Haar-Like Features

15. Detect if there is a face!

16. OpenCV Face Detection, Visualization

17. Q&A
Any question?

18. Thanks.