1. LYU0203 Smart Traveller with Visual Translator for OCR and Face Recognition Supervised by Prof. LYU, Rung Tsong Michael Prepared by: Wong Chi Hang Tsang Siu Fung Department of Computer Science & Engineering The Chinese University of Hong Kong

2. Outline Introduction Overall Design Korean OCR Face Detection Future Work

3. Introduction – What is VTT? Smart Traveller with Visual Translator (VTT)  Mobile Device which is convenient for a traveller to carry Mobile Phone, Pocket PC, Palm, etc.  Recognize and translate the foreign text into native language  Detect and recognize the face into name

4. Introduction – Motivation More and more people have mobile device which include Pocket PC, Palm, mobile phone. Mobile Device becomes more powerful. There are many people travelling aboard

5. Introduction – Motivation (Cont.) Types of programs for Mobile Device  Communication and Network  Multimedia  Games  Personal management  System tool  Utility

6. Introduction – Motivation (Cont.) Application for traveller?  Almost no!!! Very often, travellers encounter many problems about unfamiliar foreign language Therefore, the demand of an application for traveller is very large.

7. Introduction – Objective Help travellers to overcome language and memory power problems Two main features:  Recognize and translate Korean to English (Korean is not understandable for us)  Detect and recognize the face (Sometimes we forget the name of a friend)

8. Introduction – Objective (Cont.) Target of Korean OCR  Signs and Guideposts Printed Characters Contrast Text Color and Background Color Target of Face Recognizer  One face in photo  Frontal face  Limited set of faces

9. Introduction – Objective (Cont.) Real Life Examples  Sometimes we lose the way, we need to know where we are.  Sometimes we forget somebody we met before.

10. Overall Design of VTT System GUI Camera API Camera Korean OCRFace Recognizer Face Database Stroke Database & Dictionary Request Data RequestOutput User QueryResultQueryUpdateResult Request Response

11. KOCR – Design

12. KOCR – Text Area Detection Edge Detection using Sobel Filter -2 000 121 01 -202 01

13. KOCR – Text Area Detection (Cont.) Horizontal and Vertical Edge Projection Hor izon tal Proj ecti on Threshold Vertical Projection

14. KOCR – Binarization Color Segmentation  Base on Color Histogram Threshold

15. KOCR – Stroke Extraction Labeling of Connected Component with 8- connectivity

16. KOCR – Stroke Extraction (Cont.) Why do we choose stroke but not whole character?  Korean Character is composed of Some Stroke types  Limited Set of Stroke Types in Korean

17. KOCR – Stroke Feature Our Proposed Feature  Five rays each side  Difference of adjacent rays (-1 or 0 or 1)  Has holes (0 or 1)  Dimension ratio of Stroke (width/height) (-1 or 0 or 1)

18. KOCR – Stroke Feature (Cont.) Problems Faced  Train the stroke database needs much time  Two or more strokes maybe stick together

19. KOCR – Stroke Recognition Exact Matching by Pre-learned Stroke Features Trained Decision Tree

20. KOCR – Pattern Identification Six Pattern of Korean Character Identify by simple if-then-else statement 0 1 2 3 4 5

21. Face Detection Outline 1. Find Face Region 2. Find the potential eye region 3. Locate the iris 4. Improvement

22. 1. Find Face Region There are three methods available 1. Projection of the image 2. Base on gray-scale image 3. Color-based model

23. 1. Find Face Region -Projection of the image Consider only one single color: blue, green or red. Usually blue pixel value is used because it can avoid the interference of the facial feature. Project the blue pixel vertically to find the left and right edge of face.

24. 1. Find Face Region (Cont.) -Projection of the image x Edge of face Sum of pixel value

25. 1. Find Face Region ( Cont. ) -Projection of the image The image should be filtered out the high frequency of this curve by FTT (Fast Fourier Transform) Assume the face occupy large area of the image

26. 1. Find Face Region -Base on gray-scale image No color information Pattern recognition

27. 1. Find Face Region -Color-based model We use this method because of its simplicity and robustness. Color-based model is used to represent color. Since human retina has three types of color photoreceptor cone cell, color model need three numerical components.

28. Color-based model ( Cont. ) There are many color model such as RGB, YUV (luminance-chrominance) and HSB (hue, saturation and brightness) Usually RGB color model will be transformed to other color model such as YUV and HSB.

29. Color-based model ( Cont. ) -YUV We use YUV or YC b C r color model. Y component is used to represent the intensity of the image C b and C r are used to represent the blue and red component respectively.

30. Color-based model ( Cont. ) -YC b C r Image Y Cb Cr Original Image -

31. Representation of Face color How can YUV color model represent face color? What happens when we transform the pixel into Cr-Cb histogram?

32. Representation of Face color We just use a simple ellipse equation to model skin color. Cb Cr

33. Representation of Face color where L is the length of the long axis and S is the length of the short axis. We choose L = 35.42, S = 20.615, θ = - 0.726 (radius) The equation of the ellipse :

34. Representation of Face color - Color segmentation The white regions represent the skin color pixels

35. Representation of Face color -Color segmentation (modified version1) We distribute some agents in the image uniformly. Then each agent will check whether the pixel is a skin-like pixel and not visited by the other agent. If yes, it will produce 4 more agents at its four neighboring points. If no, it will moved to one of its four neighboring points randomly.

36. Representation of Face color ( Cont. ) -Color segmentation (modified version1) This agent produce 4 more agents If the pixel is a skin-like pixel and not visited by the other agent, produce 4 more agents at its four neighboring points

37. Representation of Face color ( Cont. ) -Color segmentation (modified version1) Otherwise, it will moved to one of its four neighboring points randomly This agent move to one of four neighboring point

38. Representation of Face color ( Cont. ) -Color segmentation (modified version1) Each agent will search their own region Each region are shown in the next slide with different color.

39. Representation of Face color ( Cont. ) -Color segmentation (modified version1) The advantage of this algorithm is that we need not to search the whole image. Therefore, it is fast.

40. Representation of Face color ( Cont. ) -Color segmentation (modified version1) 19270 of 102900 pixels is searched (about 18.7%) There are 37 regions

41. 2. Eye detection After the segmentation of face region, we have some parts which are not regarded as skin color. They are probably the region of eye and mouth We only consider the red component of these regions because it usually includes the most information about faces.

42. 2. Eye detection ( Cont. ) We extraction such regions by pseudo- convex hull.

43. 2. Eye detection ( Cont. ) We do the following on the regions of potential eye region 1. Histogram equalization 2. Threshold

44. 2. Eye detection ( Cont. ) Histogram equalization Threshold with < 49 After the histogram equalization and threshold, the searching space of eyes is greatly reduced.

45. 3. Locate the iris After the operations above, we almost find the eye. However, we should locate the iris. We use the following different methods  Template matching  Hough Transform

46. 3. Locate the iris ( Cont. ) -Template matching It bases on normalized cross-correlation. It is used to measure the similarity between two images

47. 3. Locate the iris ( Cont. ) -Template matching Let I 1, I 2 be images of the same size. I 1 (p i ) = a i, I 2 (p i ) = b i NCC(I 1, I 2 ) lies on the range [-1, 1]

48. 3. Locate the iris ( Cont. ) -Template matching We use this template and calculate the NCC. This template can be obtained by averaging all the eye image.

49. 3. Locate the iris ( Cont. ) -Template matching Red region show the result

50. 3. Locate the iris ( Cont. ) -Hough transform Hough Transform can find the complete shape of the edge according to small portion of edge information. It works with a parametric representation of the object we are looking for. We use Hough transform with 2D circle parametric representation to find the iris.

51. 3. Locate the iris ( Cont. ) -Hough transform We find the edge of eye by Sobel filter.

52. 3. Locate the iris ( Cont. ) -Hough transform We apply a circle on the edge image and count the number of pixel lying on the circle

53. 3. Locate the iris ( Cont. ) -Hough transform A(x,y,r) <- Number of pixel where A(x,y,r) is Accumulator, where x,y are the coordinate of the center and r is the radius of the circle. The searching space for the circle is [x, y, r] = [17, 17, 8].

54. 3. Locate the iris ( Cont. ) -Hough transform We have tried this method It fails to find the iris

55. 4. Improvement Skin Color Detection  Neuron Network with simplified activate function (polynomial)  Probability function (e.g. Bayesian estimation) Setup face Shape model it estimates the shape of face

56. 4. Improvement ( Cont. ) Template Matching Replace it with deformable template or probability function.

57. Future Work Stroke Combination Dictionary Face Detection Improvement Face Recognition  normal luminance light source  about 20 people  > 90 % accuracy Port the system into Pocket PC

58. Q&A

59. ~The End~