Biometrics of Cut Tree Faces

1. Biometrics of Cut Tree Faces William Barrett San Jose State University November 26, 2007 Biometrics of Cut Tree Faces -- W. A. Barrett

2. The Problem • Theft of valuable timber from our national forests. • Most harvesters and sawmills cooperate with the Service and operate honorably, but a few individuals or groups will steal timber from the forests from time to time. • The crime is grand theft of public property, but some thieves are willing to take the risk. Biometrics of Cut Tree Faces -- W. A. Barrett

3. A Biometric Solution? • Biometrics is the science and engineering of identification of persons and other objects through distinguishing characteristics. • A biometric identification typically begins with a collection of photographic images • a stump face and • various log faces as seen in a lumber yard. • We developed the Logface Biometric System (LBS) to match a stump to a cut face image, from a database of candidate images. Biometrics of Cut Tree Faces -- W. A. Barrett

4. The Face-matching Process • Face images are captured with an ordinary digital cameras. No special training required for this. • Annotated images are sent to a service center. • Service center installs each image along with its annotation in an LBS database. • Service center segments each log face in LBS. • LBS computes a biometric code for each face, and can display a set of faces whose codes are nearest to some candidate stump face. Biometrics of Cut Tree Faces -- W. A. Barrett

5. Segmentation • Each log face (and stump face) must be segmented, i.e. • Separated from any background pixels • LBS provides a segmenting tool using interactive graphics for the purpose • The tool permits drawing a closed cubic spline to be fit around the face. Biometrics of Cut Tree Faces -- W. A. Barrett

6. Cubic Spline Tool Any of the control points can be grabbed and moved about to fit a log face. The spline is constrained to be a simple closed curve with no cross-overs. Biometrics of Cut Tree Faces -- W. A. Barrett

7. Auto-segmentation • Automatic segmentation would be better. • We have been unable to develop an automatic tool of reasonably high quality. • Stump faces are rarely circular • The background is usually variegated • Face coloration and textures vary considerably Biometrics of Cut Tree Faces -- W. A. Barrett

8. Biometrics of Cut Tree Faces -- W. A. Barrett

9. Image Normalization • The segmentation is carried as a closed, simple polygon in a database, along with the image file and annotation. • The polygon center of gravity is considered an origin center for moment calculations • A circle whose area is equal to the polygon’s area is considered to be the moment circle • Image reduced to grayscale for moment calculations • Any pixels inside the moment circle, but outside the polygon, are set to 0. Biometrics of Cut Tree Faces -- W. A. Barrett

10. Biometric Code • A biometric code is a small vector of numbers characteristic of a given face. • For LBS, the code must be invariant with respect to rotation. • The vector members should be both individually significant and reasonably independent. • A biometric distance must be computable. • The distance between two different faces should be large • The distance between two different images of the same face should be small. Biometrics of Cut Tree Faces -- W. A. Barrett

11. Biometric Code • For LBS, we’ve chosen a set of pseudo-Zernike moments Zpq, and a set of invariants based on those moments (Mukundan [1]) Biometrics of Cut Tree Faces -- W. A. Barrett

12. Pseudo-Zernike Moment • f(r,  ) is a pixel intensity at radius r and angle  , 0  r 1. • Rpq( r ) is a pseudo-Zernike polynomial. • p  0, 0  q  p. • Too complicated for a slide presentation • see Mukundan [1] and Chong [2] • A few polynomials are given next • These can be pre-computed Biometrics of Cut Tree Faces -- W. A. Barrett

13. A Few pseudo-Zernike Polynomials The polynomial coefficients increase very rapidly with p, q Biometrics of Cut Tree Faces -- W. A. Barrett

14. Invariants • Zpq is a complex number, • but not rotationally invariant. • Certain combinations of Zpq are, for example: see Belkasim [3] Biometrics of Cut Tree Faces -- W. A. Barrett

15. Invariants • Both the real and imaginary parts of PZMI are invariant with respect to rotation. • The real part is invariant with respect to reflection, while the imaginary part is not. • Notice the fractional powers. These help control loss of precision. • We use 0  p  6, for a total of 40 orders Biometrics of Cut Tree Faces -- W. A. Barrett

16. Biometric Distances • We use an Euclidean Distance to estimate the difference between two biometric codes: •  is a vector of weights, • x is a candidate code vector • xk is a database code vector Biometrics of Cut Tree Faces -- W. A. Barrett

17. Euclidean Weights • The weights  are needed to make each of the biometric components approximately equally weighted. • We use a training set to estimate the variance in each vector component • j is the inverse variance of component j. • Each component must demonstrate some variation, or it is rejected. Biometrics of Cut Tree Faces -- W. A. Barrett

18. Significance of Training Set • In some biometric systems, the training set is used to infer an optimal biometric. • In LBS, it is only used to estimate the weights of the set of the moment invariants. • The distance weights should therefore be stable, independent of particular sets of field log faces. Biometrics of Cut Tree Faces -- W. A. Barrett

19. Face Matching • After a set of faces and stumps have been segmented, LBS provides a simple face-matching tool. • A stump face is selected. • LBS then produces a set of near-matches to the stump, ordered by increasing Euclidean distance. Biometrics of Cut Tree Faces -- W. A. Barrett

20. Face Matching Biometrics of Cut Tree Faces -- W. A. Barrett

21. Normalized Rotation • The stump and matching face are scaled to match in radius, since the camera images are not physically calibrated. • They are seldom seen at the same angle, hence... • A normalized rotation angle is computed for each • The matching face is digitally rotated by the difference angle • If the two faces indeed match, then they should also appear aligned in this view. Biometrics of Cut Tree Faces -- W. A. Barrett

22. LBS Tool Experience • A computer-astute person in the San Dimas office of the Forest Service was easily trained in its use within an hour. • The “central bureau” concept is nevertheless important – • field training and use invites variations and errors, • a central bureau can track different forests across several regions Biometrics of Cut Tree Faces -- W. A. Barrett

23. Biometric Quality • To judge the quality of the biometric matching, we need • a set of manually matched faces • with several images of each physical faces • and several different physical faces • We used 11 images, with 68 total faces, and 8 separate faces • Four faces appear on every image Biometrics of Cut Tree Faces -- W. A. Barrett

24. Biometric Quality • Because these are manually classified, we can distinguish between matched pairs and unmatched pairs. • The corresponding distributions are called the authentics and the imposters, respectively. • LBS provides the tools to carry all this out, along with an Excel-style table. Biometrics of Cut Tree Faces -- W. A. Barrett

25. Biometrics of Cut Tree Faces -- W. A. Barrett

26. Significance of Distribution • A small distance implies a high probability of a match, while a large distance implies a low match probability. • The cross-over point, about 15, can be used to distinguish a “match” from a “non-match”. • Our small set shows a cross-over probability of 0.04, which indicates that a matching sample should be seen as the top candidate, given about 25 in a random sample of candidates. Biometrics of Cut Tree Faces -- W. A. Barrett

27. Summary • A software tool has been developed for the matching of cut log faces. • It requires manual segmentation at present, though we hope to find a quality auto-segmentation algorithm in time. • An orientation-invariant transform based on the pseudo-Zernike polynomials is used to obtain a good biometrics measure. • More details, and a sample LBS system are at: http://www.engr.sjsu.edu/wbarrett Biometrics of Cut Tree Faces -- W. A. Barrett

28. Acknowledgments • We thank – • The U.S. Dept. of Agriculture, Forest Service • Ed Messerlie, Forest Service, San Dimas, CA • Andy Horcher, Forest Service, San Dimas, CA • LBS was developed under a private contract with the U.S.D.A. Biometrics of Cut Tree Faces -- W. A. Barrett

29. Non-circular Stump Face Biometrics of Cut Tree Faces -- W. A. Barrett

30. Confusion with peeled log sides Biometrics of Cut Tree Faces -- W. A. Barrett

31. Saw Kerf Texture vs. Rings Biometrics of Cut Tree Faces -- W. A. Barrett

32. Variation in face color and shape Biometrics of Cut Tree Faces -- W. A. Barrett

33. Cuts are Seldom Clean Biometrics of Cut Tree Faces -- W. A. Barrett