1. A Novel Approach Using PCA And SVM for Face Detection
A Novel Approach Using PCA And SVM for Face
Detection
S.M. Jaisakthi
September 9, 2009
2. A Novel Approach Using PCA And SVM for Face Detection
Introduction
Intoduction
Face Detection is
Pattern recognition problem
Applicable in Bankcard Identification System,Security
Monitoring,Computer Vision etc.
Difficult Problem
Broadly classified as
Appearence Based Approach
Feature Based Approach
Moment Based Approach
3. A Novel Approach Using PCA And SVM for Face Detection
Algorithm
Algorithm
In the proposed algorithm
1 Identifies the face potential area
2 Calculates eigenvector using PCA
3 Obtained eigenvector is trained with SVM
4. A Novel Approach Using PCA And SVM for Face Detection
Algorithm
Face Potential Area
Face Potential Area
Pixel character is different for face and non-face image
scan the testimage using sliding window and crop the image
of specified size
calculate histogram distribution for each subimage
face and non-face area has different histogram distribution
face is then cropped
5. A Novel Approach Using PCA And SVM for Face Detection
Algorithm
Face Potential Area
Histogram Distribution
6. A Novel Approach Using PCA And SVM for Face Detection
Principal Component Analysis
Principal Component Analysis(PCA)
Common techinque for finding patterns
Compresses a set of high dimensional vectors into a set of
lower dimensional vectors
Computing PCA
Organize the data set
Calculate the empirical mean
Calculate the deviations from the mean
Find the covariance matrix
Find the eigenvectors and eigenvalues of the covariance matrix
Sort the eigenvalues and the corresponding eigenvectors
Select first d≤n eigenvectors
The projected test image is compared to every projected
training image by using similarity measure
7. A Novel Approach Using PCA And SVM for Face Detection
Principal Component Analysis
Support Vector Machine
Finds optimal hyperplane that best separates two class
Find support vector inorder to find optimal hyperplane
Non-linear case, kernal functions are used
8. A Novel Approach Using PCA And SVM for Face Detection
Principal Component Analysis
Face detection
1 Face potential area is selected
2 PCA is used to decrease the dimension of face feature space.
3 SVM is used as classifier
9. A Novel Approach Using PCA And SVM for Face Detection
Principal Component Analysis
Conclusion
Results in high performance
saves detection time
10. A Novel Approach Using PCA And SVM for Face Detection
Principal Component Analysis
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