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

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Presentation on theme: "Real-Time Detection, Alignment and Recognition of Human Faces Rogerio Schmidt Feris Pattern Recognition Project June 12, 2003."— Presentation transcript:

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

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

3 Introduction DetectionAlignmentRecognition

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

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

6 Face Detection State-of-the-art: Learning-based approaches State-of-the-art: Learning-based approaches Neural Nets [Rowley et al, PAMI 98] Neural Nets [Rowley et al, PAMI 98] SVMs [Heisele and Poggio, CVPR 01] SVMs [Heisele and Poggio, CVPR 01] Boosting [Viola and Jones, ICCV 01] Boosting [Viola and Jones, ICCV 01] Want to know more? 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 State-of-the-art: Deformable Models Bunch-Graph approach [Wiskott, PAMI 98] Bunch-Graph approach [Wiskott, PAMI 98] Active Shape Models [Cootes, CVIU 95] Active Shape Models [Cootes, CVIU 95] Active Appearance Models [Cootes, PAMI 01] Active Appearance Models [Cootes, PAMI 01]

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

11 Face Alignment Problem: Partial Occlusion Problem: Partial Occlusion Active Wavelet Networks (AWN) (submitted to BMVC03) Active Wavelet Networks (AWN) (submitted to BMVC03) 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 State-of-the-art: Subspace Techniques PCA, FDA, Kernel PCA, Kernel FDA, ICA, etc. PCA, FDA, Kernel PCA, Kernel FDA, ICA, etc. Want to know more? Want to know more? Face Recognition: a Literature Survey [W. Zhao, 2000]

15 Face Recognition Preprocessing Preprocessing Line up eyes, histogram equalization, masking Subspace Training (PCA) Subspace Training (PCA) Classification (Nearest-neighbor) Classification (Nearest-neighbor)

16 Face Recognition

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

21 Face Recognition

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