Cozzella presentation ICAPMMOMI 2010


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International Congress on Advanced in Phase Measurement Methods in Optics Metrology and Imaging - Locarno May 2010

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Cozzella presentation ICAPMMOMI 2010

  1. 1. 17 - 21 May 2010 At Monte Verita, Locarno, Switzerland<br />IR Fringe Projection<br /> for<br /> 3D Face Recognition<br />Giuseppe Schirripa Spagnolo, Lorenzo Cozzella, Carla Simonetti<br />Dipartimento Ingegneria Elettronica, Università di Roma Tre, Italy <br />e-mail: cozzella@ -<br />
  2. 2. Overview<br /><ul><li>Face Recognition problem
  3. 3. SET-UP
  4. 4. Phase Unwrapping and inconsistent region
  5. 5. 3D dataset allignement
  6. 6. Conclusion</li></li></ul><li>Face Recognition<br />“ There is no landscape that we know as well as the human face. The twenty-five-odd square inches containing the features is the most intimately scrutinized piece of territory in existence, examined constantly, and carefully, with far more than an intellectual interest. ”<br /> - by Gary Faigin.<br />Faces are integral to human interaction.<br />Manual facial recognition is already used in everyday authentication applications<br /><ul><li> ID Card systems (passports, health card, driver’s license, etc. )
  7. 7. Surveillance operations (for instance in check point)</li></li></ul><li>Advantages of face recognition<br /><ul><li>Photos of faces are widely used in passports and driver’s licenses where the possession authentication protocol is augmented with a photo for manual inspection purposes; therefore, there is wide public acceptance for this biometric identifier.
  8. 8. Face recognition systems are the least intrusive from a biometric sampling point of view, requiring no contact, nor even the awareness of the subject.
  9. 9. The biometric works, or at least works in theory, with legacy photograph data-bases, videotape, or other image sources
  10. 10. Face recognition can, at least in theory, be used for screening of unwanted individuals in a crowd, in real time.
  11. 11. It is a fairly good biometric identifier for small scale verification applications.</li></li></ul><li>Disadvantages of face recognition<br /><ul><li>A face needs to be well lighted by controlled light sources in automated face authentication systems
  12. 12. Face currently is a poor biometric for use in a pure identification protocol
  13. 13. An obvious circumvention method is disguise
  14. 14. There is some criminal association with face identifiers since this biometric has long been used by law enforcement agencies</li></li></ul><li>... continue Face Recognition<br />
  15. 15. Imaging<br /><ul><li>Single image (2D & 3D)
  16. 16. Video sequence
  17. 17. Infrared & near infrared</li></ul>(facial thermogram)<br />
  18. 18. Facial Recognition<br /><ul><li>Facial recognition requires 2 steps:
  19. 19. Facial Detection
  20. 20. Facial Recognition
  21. 21. Typical Facial Recognition technology automates the recognition of faces using one of two 2 modeling approaches:
  22. 22. Face appearance
  23. 23. 2D Eigen faces
  24. 24. 3D Morphable Model
  25. 25. Face geometry
  26. 26. 3D Expression Invariant Recognition</li></li></ul><li>2D vs 3D<br /><ul><li>2D face recognition methods are sensitive to lighting, head orientations, facial expressions and makeup.
  27. 27. 2D images contain limited information
  28. 28. 3D Representation of face is less susceptible to isometric deformations (expression changes).
  29. 29. 3D approach overcomes problem of large facial orientation changes</li></li></ul><li>3D Face Recognition<br />
  30. 30. The system<br />In this work, the 3D model of the face is achieved by projecting near infrared light modulated by a sinusoidal fringe pattern on the face. <br />
  31. 31. ... continue The system<br />In this work, the 3D model of the face is achieved by projecting near infrared light modulated by a sinusoidal fringe pattern on the face. <br />
  32. 32. ... continue The system<br />Structured light is obtained by the interference of the two fields diffracted by a saw-tooth phase grating. The fringe patterns, distorted by the surface roughness, are captured by a high-resolution image camera.<br />
  33. 33. ... continue The system<br />The fringe patterns, captured by the image camera, are processed with the aid of the Fourier transform analysis and a procedure of unwrapped phase-map able to minimize and to fill holes generate by shadows and facial hair (like beard, mustache). <br />
  34. 34. Phase Unwrapping <br />
  35. 35. Example of reconstruction<br />Reconstructed surface<br />Original surface<br />Curves extracted<br />
  36. 36. ... continue The system<br />The method is experimentally simple, has a low-cost set-up, requires only single image as input, is easy to be integrated in systems of control and access.<br />The system can work in real time (necessity of an only acquisition) and projecting non visible light (no damages to the retina).<br />The system can be easily hidden so that it is difficult to discover.<br />
  37. 37. Face Capture System SET-UP<br />Set up details can be found in:<br />G. Schirripa Spagnolo, D. Ambrosini, "Diffractive optical element-based profilometer for surface inspection", Optical Engineering 40, pp. 44-52 (2001)<br />
  38. 38. Face Capture System - SET-UP<br />
  39. 39. Face Capture System - SET-UP<br />
  40. 40. Procedure <br />
  41. 41. Phase Unwrapping <br />and inconsistent region <br />The case of an image containing regions without phase information<br />
  42. 42. Interpolation of phase data in regions of inconsistent<br /><ul><li>Phase inconsistencies are handled by excluding invalid pixels from the unwrapping process through the assignment of zero-valued weights;
  43. 43. an initial approximate estimation of the phase data in the region (pixels) of inconsistent data is performed by “standard weighted least-squares algorithm”. At the inconsistent zones is assigned zero weight.
  44. 44. a rewrapping procedure is used to obtain wrapped phase map in region of inconsistence data (so to obtain a phase map without initial inconsistencies).</li></li></ul><li>Interpolation of phase data in regions of inconsistent<br />Unfortunately, least-squares algorithms produce unwrapped phase data that are not congruent with the original phase. Therefore, rewrapping the obtained phase usually gives a phase map different from the input. To manage this shortcoming, a congruence operation is needed. <br />To obtain the congruent phase a correlation procedure is used. <br />In particular, using the phase information “of good quality”, present in the original phase around to the regions of inconsistent data, a iterative procedure that uses the correlation algorithm can be performed. This procedure allows to reconstruct the congruent phase in the zone of inconsistent data. <br />
  45. 45. Rewrapped phase<br />20<br />15<br />Real unwrapped phase<br />10<br />Initial wrapped phase<br />5<br />0<br />-5<br />-10<br />-15<br />Esteemed unwrapped phase<br />-20<br />-25<br />0<br />10<br />20<br />30<br />40<br />50<br />60<br />70<br />80<br />90<br />100<br />
  46. 46. Inconsistent data<br />Reconstructed region<br />withbinarymask<br />Procedure that allows to reconstruct the congruence<br />
  47. 47. Interpolation of phase data in regions of inconsistent<br />To reduce the problem of the “standard weighted least-squares algorithm” we propose an enhancement of the technique. <br /><ul><li>an initial approximate estimation of the phase data in the region (pixels) of inconsistent data is performed by “standard” weighted least-squares algorithm. At the inconsistent zones is assigned zero weight.
  48. 48. a rewrapping procedure (plus congruence procedure) is used to obtain wrapped phase map without initial inconsistencies;
  49. 49. a new unwrapped phase map is obtained by the same algorithm used in step-a and a quality map with non-zero weights in the inconsistent zones.</li></li></ul><li>Interpolation of phase data in regions of inconsistent<br />
  50. 50. Interpolation of phase data in regions of inconsistent<br />To reduce the problem of regions of inconsistence, another approach consists to acquire (at the same time with more cameras) more images and subsequently to performed data fusion to produce a unique detailed 3D model.<br />
  51. 51. Interpolation of phase data in regions of inconsistent<br />Align two partially-overlapping meshesgiven initial guessfor relative transform<br />
  52. 52. Iterative Closest Point (ICP)<br /><ul><li>Iterative Closest Point (ICP) is an algorithm employed to match two clouds of points. This matching is used to reconstruct 3D surfaces from different scans, to localize robots, etc.
  53. 53. The algorithm is very simple and is commonly used in real‐time. It iteratively estimates the transformation (translation, rotation) between two raw scans.
  54. 54. Inputs: two raw scans, initial estimation of the transformation, criteria for stopping the iteration.</li></li></ul><li>ICP essential steps<br /><ul><li>Associate points by the nearest neighbor criteria.
  55. 55. Estimate the parameters using a mean square cost function.
  56. 56. Transform the points using the estimated parameters.
  57. 57. Iterate (re‐associate the points and so on).</li></li></ul><li>Aligning 3D Data<br /><ul><li>How to find correspondences: User input? Feature detection? Signatures?
  58. 58. Alternative: assume closest points correspond</li></li></ul><li>Aligning 3D Data<br /><ul><li>Converges if starting position “close enough“</li></li></ul><li>Aligning 3D Data<br />Beginning from the image 3D are individualized the features point that allows to calculate the rigid transformation for the alignment of the image and for the comparison with the database<br />
  59. 59. 36<br />Extraction of feature points<br />23<br />74<br />
  60. 60. Conclusion<br />Advantages :<br /><ul><li>The proposed system uses light source in the near infrared light spectrum. This will reduce the influence of other light sources.
  61. 61. Therefore, a new phase unwrapping procedure is realized to minimize and to fill holes generate by shadows and facial hair.
  62. 62. The device, in the current configuration, is able to scan human faces in a short time and the face control can be make in "invisible" way.
  63. 63. Besides using infrared radiation, the system avoids risks for the human subject submitted to the control. </li></li></ul><li>Conclusion<br />Drawbacks :<br /><ul><li> For real-time 3D shape measurement, the whole processing includes data acquisition, phase wrapping, phase unwrapping, coordinate calculation, 3D rendering, confront with database It is very difficult for a single CPU to accomplish all these procedures parallel computation it is necessary</li>