Face Recognition on the MORPH-II Database
Face Recognition on the MORPH-II Database
Morgan Ferguson
University of North Carolina at Wilmington
July 25, 2017
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Face Recognition on the MORPH-II Database
Overview
Introduction/ BackgroundMorph-II DatabaseFace Recognition
Our Work with Face RecognitionGoalsMethodsResultsAnalysis
ConclusionFuture ResearchReferences
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Face Recognition on the MORPH-II Database
Introduction/ Background
Morph-II Database
Morph-II Database
I Contains 55,134 mugshots of 13,617 individuals
I Collected over 5 years
I Ages range from 16 to 77 years
I Average of around 4 pictures per individual
I Provides race, gender, date of birth, date of arrest, age, agedifference since last picture, subject identifier, and picturenumber for each picture in the database
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Face Recognition on the MORPH-II Database
Introduction/ Background
Morph-II Database
Challenges and Improvements
I Original data yields poor accuracy ratesI Images have different sizes and faces are in different locationsI Pre-processed images!I Standardized data
Original
Modified
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Face Recognition on the MORPH-II Database
Introduction/ Background
Face Recognition
What is face recognition?
I Process of identifying a new face as a known individual orunknown individual
I Face recognition works by training some classifier on a set ofimages (training or gallery) and then matching new image(test or validation)
Figure: http://www.nec.com/en/global/solutions/safety/face_recognition/index.html
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Face Recognition on the MORPH-II Database
Introduction/ Background
Face Recognition
Eigenfaces
I Face images are projected onto a feature space (”face space”)I Face space is defined by ”eigenfaces”, or the eigenvectors of
the set of facesI Ability to learn to recognize new faces in unsupervised manner
Figure: M. Turk, A. Pentland, ”Eigenfaces for Recognition”, J. Cognitive Neuroscience, vol. 3, no. 1, 1991.
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Face Recognition on the MORPH-II Database
Introduction/ Background
Face Recognition
Fisherfaces
I Linear Discriminant Analysis (LDA)
I Maximizes distance between classes
I Supervised technique
Figure: http://www.scholarpedia.org/article/Fisherfaces
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Face Recognition on the MORPH-II Database
Introduction/ Background
Face Recognition
Local Binary Patterns (LBP) Review
I Different approach to obtaining vectors from an image
I Labels pixels of an image by analyzing the neighbors of eachpixel and considers the result as a binary number
I LBP’s have parameters such as block size and radius
Figure: http://www.scholarpedia.org/article/Local_Binary_Patterns
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Face Recognition on the MORPH-II Database
Introduction/ Background
Face Recognition
Classification and Distance Metrics
Support Vector Machine(SVM)
I Radial basis
Nearest Neighbor approach
I Euclidean distance
I Cosine distance
I Cityblock distance
I Bray-Curtis distance
I Canberra distance
I Mahalanobis Cosine distance
Figure: http://www.researchgate.net/figure/5423571_fig6_Figure-13-313-Illustration-of-distance-measures
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Goals
Goals
Reproduce Results
I Compare benchmark results with previous research
New TrialsI Test out new subsets, distance metrics, feature vectors, etc.
Optimize
I Try out different methods to improve on initial results
Analyze
I Start creating tables and graphs to compare results
I Begin to make conclusions
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Methods
Experimental Setup
1. Subset Morph-II
2. Begin with input data (pre-processed images from subsets orLBP feature vectors)
3. Break into training and testing data
4. Perform PCA (Eigenfaces) or LDA (Fisherfaces)
5. Use SVM or Nearest Neighbor
6. Classify test image as a subject from training images
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Methods
Subset Scheme 1
I Take subjects with more than 10 images each
I Randomly select 10 images for each person
I Match the number of males with the number of females (83subjects each)
I Randomly select 1 image for each subject as testing imageand designate other 9 for training
I 166 subjects and 1660 total images
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Methods
Subset Scheme 2
I Take subjects with more than 10 images each
I Randomly select 10 images for each person
I Randomly select 5 images for each subject as testing imageand designate other 5 for training
I 544 subjects and 5440 total images
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Results
Initial Subset 1 Results
Table: Accuracy Rates and Run Times for Face Recognition Algorithms(before histogram equalization)
SVM-R Euclidean CityBlock Cosine
Eigenfaces(PCA)
Accuracy 74.1% 50.6% 66.9% 59.6%Run Time
(sec)177.3 5.2 3.9 8.6
Fisherfaces(LDA)
Accuracy 86.14% 87.3% 83.1% 95.8%Run Time
(sec)165.2 7.7 6.1 13.3
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Results
More Subset 1 Results
Table: Accuracy Rates and Run Times for Face Recognition Algorithms(after histogram equalization)
SVM-R Euclidean CityBlock Cosine
Eigenfaces(PCA)
Accuracy 88.6% 69.9% 78.9% 71.1%Run Time
(sec)211.3 7.4 3.9 10.0
Fisherfaces(LDA)
Accuracy 89.2% 92.8% 89.2% 95.8%Run Time
(sec)179.4 10.8 7.3 12.8
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Results
LBP Results (PCA)
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Results
LBP Results (LDA)
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Results
Overall Subset 1 Results
Table: Face Recognition Accuracy Rates on MorphII Subset 1: Train on9, Test on 1
SVM-R (%) Euclidean (%) CityBlock (%) Cosine (%)
Eigenfaces(PCA)
88.6 69.9 78.9 71.1
Fisherfaces(LDA)
89.2 92.8 89.2 95.8
LBP + PCA87.3
(s=10, r=1)71.7
(s=10, r=1)69.3
(s=12, r=1)76.5
(s=10, r=1)
LBP + LDA88.6
(s=10, r=2)84.9
(s=14, r=2)81.3
(s=14, r=2)89.2
(s=14, r=3)
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Results
Back to the Data
Truth
Prediction
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Results
Subset 2 Results
Table: Accuracy Rates and Run Times for Face Recognition Algorithmsusing 5 training images, 5 testing
Euclidean CityBlock Cosine BrayCurtis Canberra
Eigenfaces(PCA)
Accuracy (%) 54.0 63.1 56.0 69.7 66.0Run Time
(sec)139.2 78.8 268.5 121.6 223.9
Fisherfaces(LDA)
Accuracy (%) 62.9 55.9 78.2 71.7 51.9Run Time
(sec)163.7 115.3 281.0 146.1 255.8
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Results
Subset 1 vs. Subset 2...What Happened?
SVM-R Euclidean CityBlock Cosine BrayCurtis Canberra
Eigenfaces(PCA)
Accuracy (%) 88.6 69.9 78.9 71.1 79.5 79.5Run Time
(sec)211.3 7.4 3.9 10.0 6.8 9.6
Fisherfaces(LDA)
Accuracy (%) 89.2 92.8 89.2 95.8 94.8 83.1Run Time
(sec)179.4 10.8 7.3 12.8 8.5 13.3
Euclidean CityBlock Cosine BrayCurtis Canberra
Eigenfaces(PCA)
Accuracy (%) 54.0 63.1 56.0 69.7 66.0Run Time
(sec)139.2 78.8 268.5 121.6 223.9
Fisherfaces(LDA)
Accuracy (%) 62.9 55.9 78.2 71.7 51.9Run Time
(sec)163.7 115.3 281.0 146.1 255.8
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Analysis
Size Analysis
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Face Recognition on the MORPH-II Database
Our Work with Face Recognition
Analysis
Gender Analysis
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Face Recognition on the MORPH-II Database
Conclusion
Future Research
Future Research
I How can we optimize the face recognition algorithm further?
I How does race affect face recognition?
I How much would gender and race classification as a first stepincrease accuracy rates and reduce run time?
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Face Recognition on the MORPH-II Database
Conclusion
References
References
G. Guo, G. Mu, K. Ricanek (2010)
Cross-age face recognition on a very large database: the performanceversus age intervals and improvement using soft biometric traits
20th International Conference on Pattern Recognition 2010 , 3392-3395.
DK Hayati PHM Yassin, S.Hoque and F. Deravi (2013)
Age sensitivity of face recognition algorithms
Proc of the Fourth International Conference on Emerging SecurityTechnologies (EST), 2013
M. Turk, A. Pentland (1991)
”Eigenfaces for Recognition”
J. Cognitive Neuroscience , vol. 3, no. 1, 1991.
K. Ricanek Jr. and T. Tesafaye (2006)
”MORPH: A Longitudinal Database of Normal Age-Adult Progression”
IEEE 7th International Conference on Automatic Face and GestureRecognition (FGR06) , Southampton, UK, Apr. 2006, pp. 341345.
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Face Recognition on the MORPH-II Database
Conclusion
References
The End
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