real-time detection, alignment and recognition of human faces rogerio schmidt feris pattern...
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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
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OverviewOverview
IntroductionIntroduction
FERET DatasetFERET Dataset
Face DetectionFace Detection
Face AlignmentFace Alignment
Face RecognitionFace Recognition
ConclusionsConclusions
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IntroductionIntroduction
Detection Alignment Recognition
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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.
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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
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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]
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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.
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Face DetectionFace Detection
Time: 100ms (PIV 1.6Ghz)
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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]
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Face AlignmentFace Alignment
Active Appearance Model (AAM)Active Appearance Model (AAM)
Statistical Shape Model (PCA)
Statistical Texture Model (PCA)
AAM SearchAAM Search
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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
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Face AlignmentFace Alignment
Similar performance to AAM in images under normal conditions.
More robust against partial occlusions.
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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.
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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]
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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)
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Face RecognitionFace Recognition
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Face RecognitionFace Recognition
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Face RecognitionFace Recognition
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Face RecognitionFace Recognition
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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
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Face RecognitionFace Recognition
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Face RecognitionFace Recognition
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Face RecognitionFace Recognition