Face Recognition and Biometric Systems 2005/2006 Filters

Face Recognition and Biometric Systems 2005/2006 Plan of presentation Review of available filters Filter application in various parts of automatic face recognition system Further research

Face Recognition and Biometric Systems 2005/2006 Filter grouping One pixel operations Pixel area operations Image histogram operations Image rotation & scaling Complex techniques

Face Recognition and Biometric Systems 2005/2006 One pixel operations Linear function Power function Logarithmic function Application Contrast improvement Image sharpness enhancement

Face Recognition and Biometric Systems 2005/2006 Linear function Scaling Dynamic range scaling in a chosen sections

Face Recognition and Biometric Systems 2005/2006 Power function Gamma correction Image after translation still looks naturally

Face Recognition and Biometric Systems 2005/2006 Logarithmic function Gray level compression Natural image look Partial lost of image information

Face Recognition and Biometric Systems 2005/2006 One pixel filters - example Input image Logarithm Scaling Gamma

Face Recognition and Biometric Systems 2005/2006 One pixel filters - example Input image Logarithm Scaling Gamma

Face Recognition and Biometric Systems 2005/2006 One pixel filters - example Input image Logarithm Scaling Gamma

Face Recognition and Biometric Systems 2005/2006 One pixel filters Advantages: Improvement of image contrast Better sharpness Disadvantages: Too bright pixels Difficulties with optimal parameters selection

Face Recognition and Biometric Systems 2005/2006 Area filters Lowpass filters Mean filter Gauss Median Highpass filters Roberts Prewitt Sobel Laplacian

Face Recognition and Biometric Systems 2005/2006 Lowpass filters Noise reduction Image smoothing Contour blurring

Face Recognition and Biometric Systems 2005/2006 Mean filter Linear filter Light image smoothing

Face Recognition and Biometric Systems 2005/2006 Gauss filter Filter uses power function Stronger image smoothing in a shorter time

Face Recognition and Biometric Systems 2005/2006 Median filter Nonlinear filter Good for noise removal from image without important information elimination

Face Recognition and Biometric Systems 2005/2006 Lowpass filters - example Input image Gauss Mean Median

Face Recognition and Biometric Systems 2005/2006 Highpass filters Image sharpness enhancement Contour detection In case of noisy images the errors will multiply

Face Recognition and Biometric Systems 2005/2006 Roberts filter Gradient method

Face Recognition and Biometric Systems 2005/2006 Prewitt filter Gradient method

Face Recognition and Biometric Systems 2005/2006 Sobel filter Gradient method

Face Recognition and Biometric Systems 2005/2006 Laplacian filter Method uses second derivative properties

Face Recognition and Biometric Systems 2005/2006 Highpass filters - example Input image Prewitt Roberts Sobel

Face Recognition and Biometric Systems 2005/2006 Histogram operations Stretching Fitting Equalization

Face Recognition and Biometric Systems 2005/2006 Histogram stretching Image dynamic range enlargement for image contrast & sharpness enhancement Does not work on images with characteristic histogram

Face Recognition and Biometric Systems 2005/2006 Histogram equalization Equal distribution of gray scale levels in input image Contrast enhancement

Face Recognition and Biometric Systems 2005/2006 Histogram equalization Algorithm:

Face Recognition and Biometric Systems 2005/2006 Histogram fitting Its aim is a transformation of an input histogram so it looks like the given one Image lighting unification

Face Recognition and Biometric Systems 2005/2006 Histogram fitting Algorithm: Input & output image histogram calculation (h In,h Out ) Histogram normalization Increment function calculation

Face Recognition and Biometric Systems 2005/2006 Histogram fitting Algorithm:

Face Recognition and Biometric Systems 2005/2006 Histogram - example Input image Equalization Stretching Fitting

Face Recognition and Biometric Systems 2005/2006 Histogram Minimization of lighting differences in images from different sources Image sharpness and contrast enhancement

Face Recognition and Biometric Systems 2005/2006 Image Rotation / Scaling

Face Recognition and Biometric Systems 2005/2006 Complex filters - techniques Kuwahara Canny Unsharp Masking LogAbout GammaAbout

Face Recognition and Biometric Systems 2005/2006 Kuwahra filter Nonlinear filters Good image smoothing Low contours blurring Algorithm: For each region: Result:

Face Recognition and Biometric Systems 2005/2006 Canny filter Optimal contour detection Algorithm: Gauss filter Sobel filter Borders direction described as Direction definition Pixel tracking in the direction of borders and removal of unnecessary pixels Thresholding

Face Recognition and Biometric Systems 2005/2006 Unsharp Masking Image sharpening Minor noise elimination Algorithm: I(x,y) = Gauss(I in (x,y)) I hp (x,y) = I in (x,y) – I(x,y) I hp (x,y) = 0 dla I hp (x,y) < threshold I out (x,y) = I in (x,y) + a*I hp (x,y)

Face Recognition and Biometric Systems 2005/2006 LogAbout method Contour detection improvement Highpass filter Logarithmic filter

Face Recognition and Biometric Systems 2005/2006 HistAbout method Contour detection enhancement Histogram stretching Gauss LogAbout

Face Recognition and Biometric Systems 2005/2006 GammaAbout method Contour detection improvement Gamma Gauss LogAbout

Face Recognition and Biometric Systems 2005/2006 Where use filers? Input image Detection Normalization

Face Recognition and Biometric Systems 2005/2006 Input image Problems: Noises Solution: Gauss filter Median filter

Face Recognition and Biometric Systems 2005/2006 Input image/Detection Problem: Dark image Solution: Histogram stretching Gamma correction GammaAbout

Face Recognition and Biometric Systems 2005/2006 Detection Problem: Contour detection Solution: Roberts filter Prewitt filter Sobel filter Canny’s method

Face Recognition and Biometric Systems 2005/2006 Shape normalization Problem: Lack of size unification Solution: Scaling Problem: Non frontal face Solution: Rotation

Face Recognition and Biometric Systems 2005/2006 Lighting normalization Problem: Irregular face lightning Solution: Histogram operations

Face Recognition and Biometric Systems 2005/2006 Filter usage Image quality enhancement Object detection method efficiency improvement Image normalization Lighting normalization

Face Recognition and Biometric Systems 2005/2006 What further?? Lighting normalization is still an area for research Dark image brightening

Face Recognition and Biometric Systems 2005/2006 Thank You