Age Classification from Hand Vein Patterns

Август 4, 2022

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Age Classification from Hand Vein Patterns

Содержание

2. Problem Automatic Age Estimation from Biological Features of Humans. Application Areas: HCI Systems Security Applications Forensics
3. Our Goal Age Estimation from Hand Vein Patterns Data To Be Used: Hand Vein Image Data
4. Methods TEAK effort estimator TEAK (short for “Test Essential Assumption Knowledge”) that has been proposed by
5. TEAK(The Essential Assumption Knowledge) It applied the easy path in five steps: 1) Select a prediction
6. TEAK(The Essential Assumption Knowledge) 3) Recognize when those assumption(s) are violated: Greedy Agglomerative Clustering (GAC) and
7. TEAK(The Essential Assumption Knowledge) GAC executes bottom-up by grouping test data, which are closest, together at
8. TEAK(The Essential Assumption Knowledge)
9. TEAK(The Essential Assumption Knowledge) 4) Remove those situations: When the violation situation find, tree is pruned
10. TEAK Algorithm normalizeValues(images); TestImage=selectTestImage(images); //Put all test images to the leaves of tree //Generate GAC from
11. Features Mean of colors Number of points that is smaller than mean of colors of a
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 k-NNs (image) Linear Weighted Derivative-Based k-NN (image) Simple k-NN
20. Simple k-NN feature Take 3x3 window which finds min and max values in the image. Threshold
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, JOURNAL OF IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. X,
28. Скачать презентацию

Слайд 2

Problem
Automatic Age Estimation from Biological Features of Humans.
Application Areas:
HCI Systems
Security Applications
Forensics
etc.

Слайд 3

Our 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.

Слайд 4

Methods
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

Слайд 5

TEAK(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):

Слайд 6

TEAK(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 7

TEAK(The Essential Assumption Knowledge)
GAC executes bottom-up by grouping test data, which

are closest, together at a higher level.

Слайд 8

TEAK(The Essential Assumption Knowledge)

Слайд 9

TEAK(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 10

TEAK 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);

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