1. Real-Time Detection, Alignment and Recognition of Human Faces
Rogerio Schmidt Feris
Pattern Recognition Project
June 12, 2003

2. Overview Introduction FERET Dataset Face Detection Face Alignment
Face Recognition
Conclusions

3. Introduction
Detection
Alignment
Recognition

4. Introduction Why this is a difficult problem?
Facial Expressions, Illumination Changes, Pose, etc.
Assumption: Frontal view faces
Objectives:
Develop a fully automatic system, suitable for real-time applications.
Evaluate it on a large dataset.

5. FERET DataSet 1196 different individuals Probe Sets:
FB: Different facial expressions
FC: Different illumination conditions
DUP1: Different days
DUP2: Images taken at least 1 year after

6. Face Detection State-of-the-art: Learning-based approaches
Neural Nets [Rowley et al, PAMI 98]
SVMs [Heisele and Poggio, CVPR 01]
Boosting [Viola and Jones, ICCV 01]
Want to know more?
Detecting Faces in Images: a Survey [M. Yang, PAMI 02]

7. Face Detection [Viola and Jones, 2001]
Simple features, which can be computed very fast.
A variant of Adaboost is used both to select the features and to train the classifier.
Classifiers are combined in a “cascade” which allows background regions of the image to be quickly discarded.

8. Face Detection
Time: 100ms (PIV 1.6Ghz)

9. Face Alignment State-of-the-art: Deformable Models
Bunch-Graph approach [Wiskott, PAMI 98]
Active Shape Models [Cootes, CVIU 95]
Active Appearance Models [Cootes, PAMI 01]

10. Face Alignment Active Appearance Model (AAM) AAM Search
Statistical Shape Model (PCA)
Statistical Texture Model (PCA)
AAM Search

11. Face Alignment
Problem: Partial Occlusion
Active Wavelet Networks (AWN) (submitted to BMVC’03)
Main idea: Replace AAM texture model by a wavelet network

12. Face Alignment
Similar performance to AAM in images under normal conditions.
More robust against partial occlusions.

13. Face Alignment
Using 9 wavelets, the system requires only 3 ms per iteration (PIV 1.6Ghz). In general, at most 10 iterations are sufficient for good convergence.

14. Face Recognition State-of-the-art: Subspace Techniques
PCA, FDA, Kernel PCA, Kernel FDA, ICA, etc.
Want to know more?
Face Recognition: a Literature Survey [W. Zhao, 2000]

15. Face Recognition www.cs.colostate.edu/evalfacerec/ Preprocessing
Line up eyes, histogram equalization, masking
Subspace Training (PCA)
Classification (Nearest-neighbor)

16. Face Recognition

17. Face Recognition

18. Face Recognition

19. Face Recognition

20. Conclusions Future Work:
An efficient, fully automatic system for face recognition was presented and evaluated.
Future Work:
Alignment: multiresolution search
View-based face recognition
Explicit illumination model
Live demo

21. Face Recognition

22. Face Recognition

23. Face Recognition