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• Preprocessing to enhance recognition performance in the presence of: - Illumination variations- Pose/expression/scale variations- Resolution enhancement (deblurring)
• Stand-alone recognition system• Preprocessing/recognition results
- Face Recognition Grand Challenge (NIST)
Preprocessing Overview
Current state-of-the-art face recognition systems degrade significantly in performance due to variations in pose, illumination, and blurring.
Problem:
IMAGECAPTURE
PREPROCESSINGRESTORATION/ENHANCEMENT
FACERECOGNITION
SYSTEM
Solution:
POSE CORRECTION due to mismatch in facial position, facial expression and scaleILLUMINATION CORRECTION due to mismatch in lighting conditions in both indoor and outdoor environmentsDEBLURRING due to mismatch in camera focus, camera lenses, camera resolution and motion blur
Face Recognition
• No a priori information with regards to pose orientation, camera parameters, etc
• No laser scanned images for 3D reconstruction
• No manual detection of feature points• Preprocessing & Stand-Alone Recognition
Highlights of the approach
Principle
• Find a function which maps a given test (probe) image into the correct train (gallery) image
• Approach
where M is the number of training images
• Select that is maximally bijective
)( testopttrain YfX
Mi ,...,2,1
if
)( testii YfX
Recognition Principle
• A function ’f ‘ is found which maps points in the test (probe) to equivalent points in the train (gallery)
)(XfY where
X = Test image (domain)
Y = Train image (co-domain)
= Bijective function mapping X YfOne to One and Onto
(bijection)
X Y
domain (X) f range(X)
Test Train
Inverse Estimation
)(YgX
g
where
Y = Train image (domain)
X = Test image (co-domain)
= Bijective function mapping Y X
X Y
domain (Y) g range(Y)
• A function ’g ‘ is found which maps points in the train (gallery) to equivalent points in the test (probe)
Test Train
Measure of Bijectivity
X Yf
Partition X
][
)(....)()()(....)()( 2121
X
XfXfXfXfXfXfM nn
g
where n is the total number of distinct blocks in X
Blue,Green,Cyan
)(....)()( 21 pXfXfXf Red
)(....)()( 21 pXfXfXf
Measure of Bijectivity
YX g
Partition Y
][
)(....)()()(....)()( 2121
Y
YfYfYfYfYfYfM pp
g
where p is the total number of distinct blocks in Y
Blue,Green,Cyan
)(....)()( 21 pYfYfYf Red
)(....)()( 21 pYfYfYf
Measure of Bijectivity
The Bijectivity score is given by:
'' 4321 gfgf MMMMS
fMgM
'gM
'fM
= Forward (test train)
= Adaptive Forward (test train)
= Backward (train test)
= Adaptive Backward (train test)
4321 ,,, = constants and 10
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
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0.6
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0.9
1
False Accepts
Fal
se R
ejec
ts
Yale Subset-I, Viisage SystemPreprocessing Performance
Original SetPreprocessed Set:Exhaustive SearchPreprocessed Set:Exhaustive Search with ConstraintsPreprocessed Set:Fast Search
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
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False Accepts
Fal
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Yale Subset-II, Viisage SystemPreprocessing Performance
Original SetPreprocessed Set:Exhaustive SearchPreprocessed Set:Exhaustive Search with ConstraintsPreprocessed Set:Fast Search
Preprocessing Performance
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
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False Accepts
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Yale Subset-II, Block Size-8x8, Search Region-88x88One-to-None Mapping Metric
Exhaustive SearchExhaustive Search with ConstraintsFast Search
Metric usedOne-to-None Mapping
Yale Subset-IBlock Size-8x8;Search-56x56
Yale Subset-IIBlock Size-8x8;Search-88x88
Exhaustive Search 100 % 80 %
Exhaustive Searchwith Constraints
100 % 83.33 %
Fast Search 98 % 83.33 %Table 4.2 Yale Results-Stand Alone Recognition-II
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
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False Accepts
Fal
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Yale Subset-I, Block Size-8x8, Search Region-56x56One-to-None Mapping Metric
Exhaustive SearchExhaustive Search with ConstraintsFast Search
Face recognition performance
PIE Subset- Exhaustive SearchBlock Size-8x8; Search-72x72
One-to-None Mapping One-to-One Mapping
Stand Alone Recognition Performance
91.18 % 95.59 %
Table 4.4 PIE Database-Stand Alone Recognition
PCA-based Approach recognition accuracy: 5.88 %
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
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False Accepts
Fal
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PIE Subset, Exhaustive Search Block Size-8x8, Search Region-72x72
One-to-None MappingOne-to-One MappingPCA Approach
Face recognition performance
• Algorithm based on image adaptive least squares illumination correction
Training image A Testing image B Adaptive segmentation
Image A illuminated as B Image B illuminated as A Least squares estimate of illumination
Preprocessing for Illumination Correction
Set tested (Yale)Enrollment Rate
(Commercial face recognition system)
Original 56 %
Preprocessed 90 %
Enrollment : Process of accepting the image and creating a feature set for recognition.
Preprocessing ResultsIllumination Correction
Comparison with Existing Methods
Test subset3D Morphable Model
Our algorithm
PIE frontal 99.8 % 99.6 %
•3D morphable models : •Good results (FRVT 2002). •Very complex, computationally expensive, •manual labeling of features
1. T. Vetter and V. Blanz, “Face Recognition Based on Fitting a 3D Morphable Model,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1063--1075, Sept. 2003.
Train Test (Poor Resolution) Reconstruted Train (Poor Resolution Test)
Test (Good Resolution) Reconstruted Train (Good Resolution Test)
Notre Dame Database
Preprocessing Example
Train Test (Poor Resolution)
Test-cap (Test ----> Train) Reconstructed Train (Test-cap ----> Train)
Preprocessing Example
Notre Dame Database
Recognition Example
Gallery Probe Bijective Mapping
With the correct gallery
White Region measure of bijectivity (52.91%)
Recognition Example
With the incorrect gallery
Gallery Probe Bijective Mapping
White Region measure of bijectivity (33.94%)
Conclusion and Future Work
• New algorithm for registration and illumination correction to enhance the performance of face recognition systems
• Algorithm is based on properties of the mapping between test and train data
• Mapping produces similarity scores which can be used for a stand-alone face recognition algorithm
• Extend algorithm for high resolution data• Reduce algorithm complexity