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ICDP 2011

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ICDP 2011

  1. 1. ICDP 2011 Latent Fingerprint Segmentation using Ridge Template Correlation Nathan Short, A. Lynn Abbott, Michael S. Hsiao, Edward A. Fox Virginia Tech October 11th, 2011
  2. 2. Motivation  Large sample of good quality features  Supervised acquisition of sample fingerprint  Few good quality features for matching  Low quality  Low fingerprint surface area Rolled/Plain Fingerprints Latent Fingerprints 1/29/2015
  3. 3. 1/29/2015 *Images from NIST SD27 Latent vs. Plain/Rolled Minutia Count*
  4. 4. Latent vs. Plain/Rolled Minutia Count 1/29/2015
  5. 5. Motivation (cont.)  Automated Fingerprint Identification Systems (AFIS)  Minutia based  Aimed towards Plain/Rolled fingerprint matching  Large sample size  Latent fingerprints continue to be encoded manually 1/29/2015
  6. 6. Motivation (cont.)  Latent matching  Recent work has included additional features in matching process [Jain and Feng]  minutiae, core points, ridge flow, local quality, ridge wavelength, and others  matching results much improved over minutia-only based methods  All features are extracted manually from latent prints for matching  Quality is subjective 1/29/2015
  7. 7. Fingerprint Identification  Segment Fingerprint Image  Enhance Fingerprint Ridges  Find Binary Image  Find Ridge Skeleton  Extract Minutiae  Match Sample template with database 1/29/2015
  8. 8. Traditional Segmentation  Normalize Image  Min-max  Remove areas with low variance  Compute Gradient Image  Approximate first derivative of normalized image by convolving with Sobel filter  Threshold based on average magnitude of gradient within local blocks 1/29/2015 𝛻 𝐼 = 1 𝑛2 𝑖,𝑗 ∈ 𝐵 𝐺 𝑥 𝑖,𝑗 2 + 𝐺 𝑦 𝑖,𝑗 2 𝐼 𝑀 = 1 𝑖𝑓 𝛻 𝐼 ≥ 𝑡 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝐺 𝑥 = 𝑆 𝑋 ∗ 𝐼 𝐺 𝑦 = 𝑆 𝑦 ∗ 𝐼 𝐼 = I − min(𝐼) max 𝐼 − min(𝐼)
  9. 9. Traditional Segmentation  Problems  Assumes background only contains random noise  Foreground – structure  Background – no structure  Latent prints typically have structured backgrounds  Resulting in many spurious minutiae when applying traditional AFIS feature extraction methods  Also have similar structured background noise in the fingerprint region itself 1/29/2015
  10. 10. Segmentation Method 1/29/2015 Input Fingerprint Image Input Fingerprint Image Normalize Image Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Find local ridge frequency map Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Generate ideal ridge template Find local ridge frequency map Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Generate ideal ridge template Adjust template to image mean and variance Find local ridge frequency map Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Generate ideal ridge template Adjust template to image mean and variance Take cross sectional slice orthogonal to ridge flow at anchor point within foreground region Find local ridge frequency map Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Cross-correlation of cross sectional region with ideal template Generate ideal ridge template Adjust template to image mean and variance Take cross sectional slice orthogonal to ridge flow at anchor point within foreground region Find local ridge frequency map Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Cross-correlation of cross sectional region with ideal template Generate ideal ridge template Adjust template to image mean and variance Threshold goodness of fit score to determine foreground region (quality levels) and background region Take cross sectional slice orthogonal to ridge flow at anchor point within foreground region Find local ridge frequency map Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Cross-correlation of cross sectional region with ideal template Generate ideal ridge template Adjust template to image mean and variance Threshold goodness of fit score to determine foreground region (quality levels) and background region Segmented Fingerprint Image Take cross sectional slice orthogonal to ridge flow at anchor point within foreground region Find local ridge frequency map Repeatforallblocksinfingerprintregion Generate Ideal Template Input Fingerprint Image Normalize Image Threshold normalized intensities to find initial foreground region Cross-correlation of cross sectional region with ideal template Generate ideal ridge template Adjust template to image mean and variance Threshold goodness of fit score to determine foreground region (quality levels) and background region Segmented Fingerprint Image Take cross sectional slice orthogonal to ridge flow at anchor point within foreground region Find local ridge frequency map
  11. 11. Ridge Template Generation  “Ideal” Ridge Template  Modeled by 𝑇𝑖 = sin 2𝜋𝑓𝑑𝑖 − 𝜋 2 = −cos 2𝜋𝑓𝑑𝑖, , ∀𝑗  Adjust normalized template to mean and variance of image by 𝑇𝑖 = 𝜎𝑖𝑚𝑔 ∙ 𝑇𝑖 + 𝜇𝑖𝑚𝑔, ∀𝑗 𝑓 𝑑13 = 3 Observed 𝑥-signature Ideal 𝑥-signature 1/29/2015
  12. 12. Segmentation Results 1/29/2015
  13. 13. Segmentation Results (cont.) Fingerprint Area (% of total Image) False Negatives (% of true minutiae labelled as background) NBIS 60.7 1.41 P1 60.7 0.29 P2 33.6 1.47 P3 45.2 0.69 1/29/2015
  14. 14. Line Detection  Latent fingerprint matching (Jain and Feng)  Ridge flow direction  Negative cost associated with ridge directions that do not match  lines which dominate the local ridge flow direction, decrease match score  Detect lines and remove from directional flow computation 1/29/2015
  15. 15. Line Detection  Hough-based approach  A line passing through a point (𝑥, 𝑦), 𝑦 = 𝑚𝑥 + 𝑏 is represented in Hough space as 𝑟 = 𝑥𝑐𝑜𝑠(𝜃) + 𝑦𝑠𝑖𝑛(𝜃)  Collinear spatial points are represented by intersecting curves in Hough space  Accumulator is used to find highest frequency parameters, (𝑟, 𝜃), corresponding to points occurring in image 1/29/2015
  16. 16. Line Detection Results 1/29/2015
  17. 17. Future Work 1/29/2015  Use classifier to determine background/foreground and quality, instead of threshold  Adjust template for ridge thickness  Performance results with refined directional map  Detect and remove errors caused by text in background
  18. 18. Thank you!  Questions?

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