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Real-Time Detection, Alignment and Recognition of Human Faces. Rogerio Schmidt Feris Pattern Recognition Project June 12, 2003. Overview. Introduction FERET Dataset Face Detection Face Alignment Face Recognition Conclusions. Introduction. Detection. Alignment. Recognition. - PowerPoint PPT Presentation
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Real-Time Detection, Alignment Real-Time Detection, Alignment and Recognition of Human Facesand Recognition of Human Faces
Rogerio Schmidt FerisRogerio Schmidt Feris
Pattern Recognition Project
June 12, 2003June 12, 2003
OverviewOverview IntroductionIntroduction FERET DatasetFERET Dataset Face DetectionFace Detection Face AlignmentFace Alignment Face RecognitionFace Recognition ConclusionsConclusions
IntroductionIntroduction
Detection Alignment Recognition
IntroductionIntroduction Why this is a difficult problem?Why this is a difficult problem?
Facial Expressions, Illumination Changes, Pose, etc. Facial Expressions, Illumination Changes, Pose, etc.
Assumption: Frontal view facesAssumption: Frontal view faces
Objectives:Objectives: Develop a fully automatic system, suitable for real-time Develop a fully automatic system, suitable for real-time
applications.applications. Evaluate it on a large dataset.Evaluate it on a large dataset.
FERET DataSetFERET DataSet
1196 different individuals1196 different individuals
Probe Sets:Probe Sets:
FB: Different facial expressionsFB: Different facial expressions FC: Different illumination conditionsFC: Different illumination conditions DUP1: Different daysDUP1: Different days DUP2: Images taken at least 1 year afterDUP2: Images taken at least 1 year after
Face DetectionFace Detection
State-of-the-art: Learning-based approachesState-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]Detecting Faces in Images: a Survey [M. Yang, PAMI 02]
Face DetectionFace 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.
Face DetectionFace Detection
Time: 100ms (PIV 1.6Ghz)
Face AlignmentFace Alignment
State-of-the-art: Deformable ModelsState-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]
Face AlignmentFace Alignment Active Appearance Model (AAM)Active Appearance Model (AAM)
Statistical Shape Model (PCA)
Statistical Texture Model (PCA)
AAM SearchAAM Search
Face AlignmentFace Alignment
Problem: Partial OcclusionProblem: Partial Occlusion
Active Wavelet Networks (AWN) Active Wavelet Networks (AWN) (submitted to BMVC’03)(submitted to BMVC’03)
Main idea: Replace AAM texture model by a wavelet networkMain idea: Replace AAM texture model by a wavelet network
Face AlignmentFace Alignment
Similar performance to AAM in images under normal conditions.
More robust against partial occlusions.
Face AlignmentFace Alignment
Using 9 wavelets, the system requires only 3 ms per Using 9 wavelets, the system requires only 3 ms per iteration (PIV 1.6Ghz). In general, at most 10 iterations are iteration (PIV 1.6Ghz). In general, at most 10 iterations are sufficient for good convergence.sufficient for good convergence.
Face RecognitionFace Recognition
State-of-the-art: Subspace TechniquesState-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]Face Recognition: a Literature Survey [W. Zhao, 2000]
Face RecognitionFace Recognition
www.cs.colostate.edu/evalfacerec/ www.cs.colostate.edu/evalfacerec/
PreprocessingPreprocessingLine up eyes, histogram equalization, maskingLine up eyes, histogram equalization, masking
Subspace Training (PCA)Subspace Training (PCA)
Classification (Nearest-neighbor)Classification (Nearest-neighbor)
Face RecognitionFace Recognition
Face RecognitionFace Recognition
Face RecognitionFace Recognition
Face RecognitionFace Recognition
ConclusionsConclusions An efficient, fully automatic system for face recognition was An efficient, fully automatic system for face recognition was
presented and evaluated.presented and evaluated. Future Work:Future Work: Alignment: multiresolution searchAlignment: multiresolution search View-based face recognitionView-based face recognition Explicit illumination modelExplicit illumination model Live demoLive demo
Face RecognitionFace Recognition
Face RecognitionFace Recognition
Face RecognitionFace Recognition