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A Thermal Hand Vein Pattern Verification System

A Thermal Hand Vein Pattern Verification System. Lingyu Wang & Graham Leedham. Forensic and Security Lab, Nanyang Technological University, Singapore . Outlines. Overview Background Vein patterns as biometrics Proposed system model for hand vein pattern verification Details of the system

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A Thermal Hand Vein Pattern Verification System

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  1. A Thermal Hand Vein Pattern Verification System Lingyu Wang & Graham Leedham Forensic and Security Lab, Nanyang Technological University, Singapore

  2. Outlines • Overview • Background • Vein patterns as biometrics • Proposed system model for hand vein pattern verification • Details of the system • Stage I : Data Acquisition • Stage II: ROI selection and image enhancement • Stage III: Background separation • Stage IV: Skeletonization • Stage V: Matching • Testing • Summaries and Conclusions

  3. Overview • Background • Many biometric features have been utilized for this purpose. (e.g. fingerprints, retina pattern, voice etc.) • Each of them has its strengths and weaknesses. • Vein Patterns as a Biometric Feature • A vein pattern refers to the vast network of blood vessels underneath the skin of a certain part of a person’s body • Properties of vein patterns • Uniqueness: a person’s vascular patterns are distinct. • Stability: relatively unaffected by aging, except for predictable growth as with fingerprints. The shape of the pattern keeps unchanged. • Tolerance to forgery: the blood vessels are hidden and much harder for intruders to copy. • A potentially good biometric feature…

  4. Image Acquisition Image Enhancement & ROI Selection Vein Pattern Segmentation Skeletonization Shape Match Decision Raw Images Finer Images Vein Pattern Template Database Data Collection Vein Pattern Extraction Proposed Hand Vein Pattern Verification System • We proposed a new system that recognizes the human hand vein pattern images acquired by a far-infrared (thermal) camera, which consists of five individual stages • The key difference against the others • The system directly recognizes geometric shape of a vein pattern by measuring its Line-segment Hausdorff Distance against a template

  5. Data AcquisitionStage I • Data of interest is limited to the vein pattern in the back of the hand • Less invasive • Concerns for potential integration with other hand biometrics • Vein pattern images are captured using Far-Infrared (Thermal) imaging technology • Superficial veins have higher temperature than the surrounding tissues • The thermal detector inside the Infrared camera forms images with the infrared radiation (typically within the Far Infrared region of 8~14m) emitted by the human body

  6. Images captured in an air-conditioned office environment (20-25°C and <50% humidity) Images captured in a tropical outdoor environment (30-34°C and >80% humidity) Sample Images CollectedStage I

  7. Factors affecting image quality Nearness of the vein to the surface Body temperature Unevenly distribution of heat Heat radiation Ambient temperature and humidity Focusing Camera calibration Impossible to capture the complete vascular network however, the information contained possessed by the superficial vein pattern is sufficient to perform personal verification tasks for a reasonable sized user group Analysis on the Data QualityStage I

  8. Region of Interest (ROI) LocationStage II • Technique proposed by Lin & Fan[1] • Extract the contour of the hand • Calculate the distance profile between the contour points and the midpoint of the wrist • Locate the landmark points (tips and valleys) of the hand • Define a fixed sized rectangular region as the ROI [1] Chih-Lung Lin and K.-C. Fan, Biometric Verification Using Thermal Images Of Palm-dorsa Vein Patterns. IEEE Trans. Circuits and Systems for Video Technology, 2004. 14(2): p. 199-213.

  9. Image EnhancementStage II • Removing the speckling noise using a order statistic median filter with a 5x5 neighborhood region. • Reduce the effect of undesired high frequency noise with a 2-D Low Pass Gaussian Filter with 0.8 standard deviation. • Finally, all the images are normalized so as to suppress the possible imperfections in the image due to the sensor noise and other effects, where the desired mean and variance of the images are both set to be 100.

  10. Vein Pattern SegmentationStage III • The purpose is to separate the vein pattern from the image background, and it is done by locally adaptive thresholding • The threshold is calculated at each pixel, which depends on some local statistics of the pixel neighborhood. • The threshold over here is set as the mean value of a local 13x13 neighborhood . • Morphological erosion and dilation is needed to clean up the images after thresholding.

  11. SkeletonizationStage IV • The size of the vein varies as human grows, and hence, the shape of the pattern is the sole feature for later recognition • A good representation of the pattern’s shape is via extracting its skeleton. • the vein pattern images go through the thinning algorithm to obtained its skeleton • Pruning is taken to remove the spurs branches and clean up isolated pixels

  12. Vein Pattern MatchingStage V • The purpose is to match the geometric shape of the incoming vein pattern against the template • Hausdorff Distance can be calculated for the spatial similarity of the vein patterns • lacks local structure representation such as orientation when it comes to comparing the shapes of curves • Line-Segment Hausdorff Distance (LHD) is calculated instead. • Line Segment Hausdorff Distance (LHD) is firstly used in a face matching application by Gao and Leung [2] • Incorporates the structural information of line segment orientation • More effective for comparisons of shapes consisting of a number of curve segments [2] Y.Gao and M.K.H. Leung, “Line Segment Hausdorff Distance on Face Matching”. Pattern Recognition. 35 (2002) 361-371.

  13. LHD For Vein Pattern Shape MatchingStage V • A number of sampling points are taken on the skeleton of the vein pattern • Using the sampling points as end points, the shape of the vein pattern is then represented by a set of line segments • Comparisons between the testing pattern and the template is carried out based on the calculation of vector d. • The directed and undirected LHD is then defined as h and H respectively on the rightside.

  14. Testing Results • Database containing 108 images from 12 people (9 for each person) • 3 images for each person were selected randomly to form the templates for that person (overall template set size 36) • The rest is used as the testing set (size 72) • At verification stage, three undirected LHDs (H1, H2, H3) are computed between the testing vein pattern and the three templates. • The Average value H’ of H1, H2 and H3 is the similarity measure.

  15. Testing Results • Choosing 9.0 as the threshold, all the images in the testing set are correctly recognized • The results of the experiment are encouraging • However, the images are taken in a more controlled manner • For a real life application, the surrounding conditions are unknown • The image quality of the vein pattern may reduce, and as a result, a decrease of verification accuracy can be expected

  16. Summaries and Conclusions • A personal verification system using the Far-Infrared vein pattern in the back of the hand as the biometric feature is proposed • Unlike other approaches, the system directly recognizes the geometric shapes of the vein patterns • The testing results of the system are encouraging • A potentially good biometrics to be integrated with other hand based system to become a multi-modal biometric system.

  17. Alternative Imaging TechnologyStage I • Near-Infrared Imaging • Near Infrared region refers to the spectrum from 0.7um to 1.4 um • The haemoglobin in veinous blood absorbs more of the incident IR radiation than the surrounding tissue • Thus appearing darker when viewed on a conventional video monitor • Under Investigation…

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