Application of Conformal Geometric Algebra to In-plane Rotated Face Detection by AdaBoost-based Algorithm

NetBean project:

- 20180228_gauss_yi: implementation of AdaBoost with Gaussian distribution
	- Outputs:
		- 20180228_gauss.wk (for Frontal Face Detection)
		- 20180321_mv_gauss.wk (for Multi-view Face Detection)

- 20180228_vj_yi: implementation of AdaBoost by Viola-Jones method
	- Outputs:
		- 20180301_vj.wk
		- 20180321_mv_vj.wk

- 20180320_cga_1_yi: implementation of AdaBoost with CGA 1-D
	Outputs:
		- 20180320_cga_1.wk
		- 20180321_mv_cga_1.wk

- 20180320_cga_2_yi: implementation of AdaBoost with CGA 2-D
	Outputs:
		- 20180322_cga_2.wk
		- 20180321_mv_cga_2.wk

- 20180320_cga_3_yi: implementation of AdaBoost with CGA 3-D
	Outputs:
		- 20180322_cga_3.wk
		- 20180322_mv_cga_3.wk

For each project: frontal and MV Face Detection are involved. Just change dataset to train each model
Each Strongclass (.wk file) mentioned above consists of 200 Weakclass 

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Program structure:

main
|__ trainProcess
|__ testProcess

Training Process:

trainProcess
|__ readInputImage
|__ prepareIntegralImage
|__ prepareFeatureIndex
|__ algorithmAdaboost
|__ saveArrWeakClass

Functions:

ArrayList<int[][]> readInputImage(String path)
	Parameter:
		- path: directory path to get input images
	Usage:
		- Read and pre-process input image
	Step:
		- Read images from given path
		- Convert to Grayscale
		- Normalize image
		- Return an ArrayList


void prepareIntegralImage()
	Usage: 
		- Compute integral images
	Step:
		- Get Integral images and store in an ArrayList


void prepareFeatureIndex()
	Usage:
		- Read information of Haar-like pattern
	Step:
		- Read Color map
		- Read Feature info from Lookup table
		(color map and lookup table are stored in .txt file at specific path in project)

WeakClass[] algorithmAdaboost()
	Usage:
		- Train Strongclass from given Integral images and Haar-like pattern
		- Each WeakClass is chosen from 5000 random features
	Step:
		- Initialize Weight for Face and Non-face samples
		- For each round:
			- Normalize Weights
			- Get Feature with smallest error
			- Calculate Alpha
			- Update Weights

Feature getFeatureIndex(double[] w_p, double[] w_n)
	Parameter:
		- w_p: Weights of Face
		- w_n: Weights of Non-face
	Usage:
		- Choose the Feature that yields the smallest error rate with given Weights for Face and Non-face
	Step:
		- Get K (=5000) random features
		- For each feature:
			- Construct CGA cluster for Face and Non-face
			- Find distance from each sample to each region to classify
			- Compute error to choose feature producing least error rate

double[] getFeatureValue(ArrayList<int[][]> lstInt, int idx)
	Parameter:
		- lstInt: list of Integral images
		- idx: index of Haar-like feature
	Usage:
		- Compute feature value for the whole set of image with given feature index
	Step:
		- Get information of Haar-like feature
		- Compute feature value by applying Color map read before

double getFeatureValueSingle(int[][] imgInt, int idx)
	Parameter:
		- imgInt: a single Integral image
		- idx: index of Haar-like feature
	Usage:
		- Compute feature value of a single image with given feature index
	Step:
		- Apply Color map to compute feature value

Test Process:

testProcess
|__ readInputImage
|__ prepareIntegralImage
|__ prepareFeatureIndex
|__ loadWeakClass
|__ testAdaBoost

Functions:

void testAdaBoost(WeakClass[] arrWeakClass, int len, double rate)
	Parameter:
		- arrWeakClass: StrongClass
		- len: the number of WeakClass used for testing
		- rate: rate used for testing (instead of using 0.5 as in origin AdaBoost algorihtm)
	Usage:
		- Test StrongClass with given number of WeakClass and rate to find TPR, FPR
	Step:
		- For each test sample:
			- Construct vector CGA_x
			- Find distance to each CGA cluster then compare to classify
		- Calculate TPR and FPR

double findGaussDist(double[] x, double x_mag2, double[][] s, double[] lambda, double[] s_inf, double[] s_0)
	Parameter:
		- x: vector CGA x
		- x_mag2: magnitude of x
		- other: eigen-values, eigen-vectors of corresponding CGA cluster
	Usage:
		- Calculate the distance to the CGA cluster
	Step:
		- Just apply formula in CGA paper