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Real-Time Detection, Alignment and Recognition of Human Faces

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Presentation on theme: "Real-Time Detection, Alignment and Recognition of Human Faces"— Presentation transcript:

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 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

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