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Age Classification from Hand Vein Patterns презентация
Содержание
- 1. Age Classification from Hand Vein Patterns
- 2. Problem Automatic Age Estimation from Biological Features
- 3. Our Goal Age Estimation from Hand Vein
- 4. Methods TEAK effort estimator TEAK (short for
- 5. TEAK(The Essential Assumption Knowledge) It applied the
- 6. TEAK(The Essential Assumption Knowledge) 3) Recognize when
- 7. TEAK(The Essential Assumption Knowledge) GAC executes bottom-up
- 8. TEAK(The Essential Assumption Knowledge)
- 9. TEAK(The Essential Assumption Knowledge) 4) Remove those
- 10. TEAK Algorithm normalizeValues(images); TestImage=selectTestImage(images); //Put all test
- 11. Features Mean of colors Number of
- 12. RESULTS Result has been evaluated by using AE(absolute Error ) and MAE (Mean AE)
- 13. RESULTS (teak+kNN) mean color
- 14. RESULTS (teak+kNN) mean color
- 15. RESULTS (teak+kNN) age estimation age group estimation
- 16. RESULTS (PCA) +own age -own age
- 17. RESULTS (PCA) +own age -own age
- 18. RESULTS SUMMARY
- 19. Methods Correlation-Based k-NN (image) Correlation of Derivative-Based
- 20. Simple k-NN feature Take 3x3 window which
- 21. Results
- 22. Feature with T=18 and k=2.
- 23. Result of AGES Algorithm(face)
- 24. Results of AAM with SVR(face)
- 25. Results of Dimensionality Reduction(face)
- 26. References [1] E. Kocaguneli and A. Bener,
- 27. Thank You. Questions
Problem Automatic Age Estimation from Biological Features of Humans. Application Areas: HCI Systems Security Applications Forensics etc.
Слайд 2Problem
Automatic Age Estimation from Biological Features of Humans.
Application Areas:
HCI Systems
Security Applications
Forensics
etc.
Слайд 3Our Goal
Age Estimation from Hand Vein Patterns
Data To Be Used:
Hand Vein
Image Data of 30 Persons mixed gender.
Age classes are as follows.
(15-20) 5 People, (20-25) 5 People, (25-30) 5 People,(30-35) 5 People, (35-45) 5 People, (45+) 5 People.
Age classes are as follows.
(15-20) 5 People, (20-25) 5 People, (25-30) 5 People,(30-35) 5 People, (35-45) 5 People, (45+) 5 People.
Слайд 4Methods
TEAK
effort estimator TEAK (short for “Test Essential Assumption Knowledge”) that has
been proposed by Ekrem Kocaguneli and Ayse Bener [1].
k-nearest neighbor
PCA
k-nearest neighbor
PCA
Слайд 5TEAK(The Essential Assumption Knowledge)
It applied the easy path in five steps:
1)
Select a prediction system: As prediction system ABE is used.
2) Identify the predictor’s essential assumption(s):
2) Identify the predictor’s essential assumption(s):
Слайд 6TEAK(The Essential Assumption Knowledge)
3) Recognize when those assumption(s) are violated: Greedy
Agglomerative Clustering (GAC) and the distance measure of equation (Euclidean) is used to identify Assumption Violation.
Слайд 7TEAK(The Essential Assumption Knowledge)
GAC executes bottom-up by grouping test data, which
are closest, together at a higher level.
Слайд 9TEAK(The Essential Assumption Knowledge)
4) Remove those situations: When the violation situation
find, tree is pruned to remove those violations. There are three types of prune policy:
5) Execute the modified prediction system.
Слайд 10TEAK Algorithm
normalizeValues(images);
TestImage=selectTestImage(images);
//Put all test images to the leaves of tree
//Generate
GAC from bottom to up
GAC1=GenerateGACTree(TrainingImages);
//Traverse tree and prune if needed
prototaypeImages=Travers1Prune(GAC1, TestImage);
//Generate Second GAC tree
GAC2=GenerateGACTree(prototaypeImages);
//Compute, estimate, the median
estimatedAge=Traverse(GAC2, TestImage);
GAC1=GenerateGACTree(TrainingImages);
//Traverse tree and prune if needed
prototaypeImages=Travers1Prune(GAC1, TestImage);
//Generate Second GAC tree
GAC2=GenerateGACTree(prototaypeImages);
//Compute, estimate, the median
estimatedAge=Traverse(GAC2, TestImage);
Слайд 19Methods
Correlation-Based k-NN (image)
Correlation of Derivative-Based k-NNs (image)
Linear Weighted Derivative-Based k-NN (image)
Simple
k-NN (1 feature)
Слайд 20Simple k-NN feature
Take 3x3 window which finds min and max values
in the image.
Threshold (max-min)
Data Set Used: Hand Palm
Threshold (max-min)
Data Set Used: Hand Palm
Слайд 26References
[1] E. Kocaguneli and A. Bener, JOURNAL OF IEEE TRANSACTIONS ON
SOFTWARE ENGINEERING, VOL. X, NO. Y, SOMEMONTH 201Z, 2010.
