1. Advanced Eigenface
Face Recognition System
Peixi Xiong
Northwestern University

2. Background
• Facial Recognition is widely used since its convenience.

3. Motivation
Problems:
• Low Recognition Rate
• Time Consuming

4. Motivation
• Database in Varying Pose
• Lack of Illumination
• Glasses Interferences
• Database in Larger scale

5. Advantage and Drawback of the PCA Face
Recognition
• Advantage
• Drawback

6. Drawback
• Time Consuming
• Varying Pose and Illumination
• Frontal Views/Face Expression and Disguise

7. Brief Analysis of the Database and the
Original System
• Good Illumination
• Varying Pose (Tilted Face )
• NOT Only the Frontal Views

8. Close All
Traversing the
File
Check
Toolbox
Build Face
Part Detector
Crop the Face
There is a
Face
Check if it is
Frontal Face
Yes, it is
Frontal Face
Check if it is
tilted
Yes, it is a
Tilted Face
Output
No, it is not.
Do Rotation
Output
Illumination
Correction*
Glasses
Removal
No, it is Side
Face
Drop It
There is no
Face
Drop It
The Pre-Processing
System

9. Build Face Part Detector/Crop the Face
• Output the Face Boundary, Left eye Boundary, Right eye Boundary,
Mouth Boundary and Nose Boundary
• The relevant information is included in the documentation of Matlab,
vision.CascadeObjectDetector System object.

10. Close All
Traversing the
File
Check
Toolbox
Build Face
Part Detector
Crop the Face
There is a
Face
Check if it is
Frontal Face
Yes, it is
Frontal Face
Check if it is
tilted
Yes, it is a
Tilted Face
Output
No, it is not.
Do Rotation
Output
Illumination
Correction*
Glasses
Removal
No, it is Side
Face
Drop It
There is no
Face
Drop It
The Pre-Processing
System

11. Detect If It Is Frontal Face
• Detect Box
• Check if Two Eyes Boxes Exist
• Location of Mouth & Nose

12. Details-Types of Side Faces

13. The Second Type
• Get Vertical Center of Mouth and Nose
• Calculate Absolute Distance of Two Middle Lines
• ‘Relative’ Distance
Relative Distance = Absolute Distance/ Face Width
• Set Threshold

14. Close All
Traversing the
File
Check
Toolbox
Build Face
Part Detector
Crop the Face
There is a
Face
Check if it is
Frontal Face
Yes, it is
Frontal Face
Check if it is
tilted
Yes, it is a
Tilted Face
Output
No, it is not.
Do Rotation
Output
Illumination
Correction*
Glasses
Removal
No, it is Side
Face
Drop It
There is no
Face
Drop It
The Pre-Processing
System

15. • Check Location of Left Eye&Right Eye
(the boundary box)
• Get Center points
• Link
• Check the Angle
• imrotate()
• Rebuild Face Part Detector&Recrop the Face
Detect If It Is Tilted Face

16. Close All
Traversing the
File
Check
Toolbox
Build Face
Part Detector
Crop the Face
There is a
Face
Check if it is
Frontal Face
Yes, it is
Frontal Face
Check if it is
tilted
Yes, it is a
Tilted Face
Output
No, it is not.
Do Rotation
Output
Illumination
Correction*
Glasses
Removal
No, it is Side
Face
Drop It
There is no
Face
Drop It
The Pre-Processing
System

17. Illumination-Correction
• 𝐼 𝑚, 𝑛 = 𝑅 𝑚, 𝑛 𝐿(𝑚, 𝑛)
• Low Frequency Component & High Frequency Component
• DCT Normalization (Discrete Cosine Transform Normalization)
• log 𝐼 𝑚, 𝑛 = log 𝑅 𝑚, 𝑛 + log 𝐿(𝑚, 𝑛))
K. P. Horn, Robot Vision. Cambridge, MA: MIT Press, 1986.

18. Details
• Installation
• Do Image Histogram Normalization
• Create & Save Zigzag Map
• Transform to Logarithm & Frequency Domains
• Set Zero to the Relevant Coefficient
• Inverse DCT
• Post-Processing

19. Result

20. Part II

21. Preprocessing
• Crop the face[1]
• Convert RGB to Grayscale

22. Edge Detection
• Not Continuous

23. Fill
• Fill the eyeglasses inner part
• Why ?
• Some flaw on edge detection

24. Method -- Imfill
• How to Compensate?

25. Method--dilate
• Expand the mask

26. Possible problem
• Nose
• Mouth
• Hair
• Other flaws
• delete the parts

27. Compensate
• Recover masked skin
• Smooth

28. Recover Masked Skin
• Get mean within 5*5 box
• Special case:
• Not only one pixel in box
• Recursively find the other pixels’ mean

29. Find Masked Pixel

30. Get Mean Value

31. Recursion
Ignore the

32. Recursion
=

33. Recover & Smooth
• Gaussian filter

34. Question?
• How to remove wrong mask

35. Skin Color Detection:
Lighting
Compensation
Color
Transformastion
Skin Color Detection
Variance-based
Segmentation
Component
Grouping
Face Boundary
Detection

36. Resize Graph

38. Comparing

39. Experimental Results Analysis
• ROC Curve
• CMC Curve

40. ROC Curve
ROC Curve of advanced
system is closer to the
broader than original system

41. CMC Curve
CMC Curve of advanced
system is closer to broader
than traditional system.

42. Main Work
• Delete Side-face
• Crop Face
• Tiled-face Rotation
• Illumination Correction
• Glasses Removal
• Properties Analysis

43. Q&A

44. Thank you!