Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/18—Eye characteristics, e.g. of the iris
  • G06V40/193—Preprocessing; Feature extraction
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/40—Extraction of image or video features
  • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
  • G06V10/478—Contour-based spectral representations or scale-space representations, e.g. by Fourier analysis, wavelet analysis or curvature scale-space [CSS]

Definitions

• the present invention relates to shape representation using Fourier Transforms.
• the invention finds particular although not exclusive application in biometrics, for example in the generation of approximate representations of the outer and/or inner boundary of the iris in the human eye.
• FFTs Fast Fourier Transforms
• a method of approximating an iris boundary comprising the steps of:
• the standard method of calculating the Fourier Series coefficients such as the Discrete Fourier Transform (DFT) or the Fast Fourier Transform (FFT) cannot be used.
• DFT Discrete Fourier Transform
• FFT Fast Fourier Transform
• this method is used to map the inner boundary of the iris (or, equivalently, the outer boundary of the pupil) of a human eye. Alternatively, it may be used to map the outer iris boundary.
• the use of higher harmonics provides excellent pupil localisation, both on general and on non-ideal eye images.
• the method provides excellent results on the vast majority of pupils which are significantly non-circular.
• a method of approximating a two-dimensional shape comprising the steps of: • Noting a plurality of spaced measured points on the shape;
• Figure 1 shows a non-circular pupil shape, as imaged; and Figure 2, shows an approximation to that shape.
• an eye may be imaged, and the image analysed to identify a plurality of points 10, which occur on the imaged pupil/iris boundary 12.
• an approximate pupil location may be first determined by searching for a dark area of significant size close to the image centre. A histogram analysis may then be carried out to find a more exact centre as well as the average pupil radius. This approximate circular pupil boundary may then be examined in detail to obtain the required number of edge points 10. In the preferred embodiment, 16 such points are identified. It will be understood by those skilled in the art that other methods may be employed to locate points on the pupil/iris boundary and the scope of the claimed subject matter is not limited in this respect. It will also be understood that the points 10 may not necessarily be equally spaced around the edge of the pupil. Indeed, in some images part of the boundary 14 may be obscured by an eyelid and/or eyelashes 16.
• the boundary points 10 can be used to generate a mathematical approximation 20 of the actual curve 12, as is shown in Figure 2.
• the fitted curve 20 is a Fourier
• a standard discrete Fourier Series such as a FFT is a least squares fit of regularly spaced data, and because of the orthogonality of the functions cos and sin results in a standard formula by which a n and b n may be calculated.
• the matrix is symmetric. This can be solved for any M and N giving an approximation by N harmonics to M given points. Many standard methods are known for solving such a system of equations and the scope of the claimed subject matter is not limited in this respect.
• the present embodiment may also be used to model the shape of the outer boundary of the iris. Once the inner and outer boundaries have been determined, biometric identification can proceed in the normal way based on the characteristics of the iris image between the inner and outer boundaries.
• an improved fit may sometimes be achieved using a multi pass approach: carry out a first fit, exclude any outliers which are greater than a cut-off value, and repeat the calculation.
• the cut-off value may be fixed, or may be data dependent, for example a given number of standard deviations .

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• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Multimedia (AREA)
• Theoretical Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• General Physics & Mathematics (AREA)
• Health & Medical Sciences (AREA)
• Spectroscopy & Molecular Physics (AREA)
• Mathematical Physics (AREA)
• General Health & Medical Sciences (AREA)
• Ophthalmology & Optometry (AREA)
• Human Computer Interaction (AREA)
• Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
• Collating Specific Patterns (AREA)
• Image Analysis (AREA)
• Image Processing (AREA)

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• 2007
  • 2007-01-17 US US11/654,496 patent/US8055074B2/en active Active
• 2008
  • 2008-01-15 EP EP08701481A patent/EP2104908B1/en active Active
  • 2008-01-15 WO PCT/EP2008/050370 patent/WO2008087127A1/en not_active Ceased
  • 2008-01-15 ES ES08701481T patent/ES2393260T3/es active Active
  • 2008-01-15 JP JP2009545905A patent/JP2010517126A/ja active Pending
  • 2008-01-15 CN CN2008800023928A patent/CN101589401B/zh active Active
  • 2008-01-15 PL PL08701481T patent/PL2104908T3/pl unknown
• 2009
  • 2009-07-30 ZA ZA2009/05324A patent/ZA200905324B/en unknown

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