Discrete Finger and Palmar Feature Extraction for Personal Authentication Junta Doi, Member, IEEE,and Masaaki Yamanaka Advisor:Wen-Shiung Chen Student: Min-Chao Chang

2 Outline  Introduction  Image acquisition  Feature Point Definition  Feature Extraction & Matching  Conclusion

3 Introduction  Biometrics  Physiological traits  Behavioral traits  finger geometry observation  Palmar flexion crease  Hand anatomy  Hand geometry is considered to achieve medium security.

4 Introduction  Advantages  No time-consumptive image analysis  Noncontact  Real-time  Reliable feature extraction  Easily combinable with other traits

5 Image Acquisition Device: using a monochrome and/or color video camera Resolution: not require for the faster response / major crease detection Propose: the palm is placed freely toward the video camera in front of a low-reflective plate

6 Image Acquisition Schematic photograph of the palm image acquisition device

7 Image Acquisition Finger alignment: use an image of the finger- close-together without bending Enhance creases: 1. By CCD camera with polarizing filter 2. Lighting from a direction of 45 degrees wrist side 3. near infrared CCD camera

8 Image Acquisition Image quality: VGA of 640X480, 8bit gray levels the number of palm images is about 500, corrected from about 50 subjects Noise reduction : use repetitive morphological operations of erosions and dilations

9 Feature Point Definition Intersection points with circles

10 Feature Point Definition Illustration of tangential line at intersection points

11 Feature Point Definition  The way of extract the skeletal line  skeletonization  thinning algorithm  The way of search the intersection points  two dimensional matrix operator

12 Feature Extraction Finger Spreading and Skeletal Lines a. the middle finger skeletal axis remains unchanged b. when fingers are bring together, the skeletal lines deviate little Feature extraction at the intersection points on skeletal lines

13 Feature Extraction Comparison of intersection points when fingers are spread apart And brought together

14 Feature Extraction Comparison of each finger skeletal line when fingers are spread apart (white lines) and wider apart (black lines).

15 Feature Extraction Orientations at the intersection pointsExamples of detected orientations at the intersection points

16 Feature Extraction missing points or additional points on the extended skeletal line in the palm region may occur in the new entry the middle finger matching is found to be the most reliable among the four

17 Feature Matching Using Skeletal Lines For the palm, it consists of the intersection points of the major palmar flexion creases or prominent creases, which are typically three palmar creases, on the extended skeletal line of each finger and also the orientations at the intersection points

18 Feature Matching Using Skeletal Lines The first feature vector( in middle finger)  Distal  Middle  Proximal The second, third and fourth feature vector  Forefinger  Ring finger  Little finger

19 Feature Matching Using A Mesh a mesh is proposed and constructed by connecting laterally the corresponding intersection points on the adjacent skeletal lines

20 Feature Matching Using A Mesh Each lateral line to line distance depends on the width of the finger The over all lateral line distances depend on the palm width All the widths and lengths are personal and are combined with the oriented palmar intersection points

21 Feature Matching Using A Mesh Mesh Matching for Authentication the middle finger skeleton is selected to align the meshes for the enrolled and the new Some deviates is caused by a palm image variation due to the palm bending, though all the fingers are brought together Compare of the enrolled and the new of the same palm

22 Feature Matching Using A Mesh Compare of mashes for different palms

23 Feature Matching Using A Mesh The mesh deviation between the two, is evaluated by calculating the root mean square deviation (rmsd) value.  δ i is the positional difference at each mesh point  N is the total number of the mesh points to be compared The magnitude of the difference is measured in pixels and thereafter normalized by the parameters of the finger length and the palm width

24 Feature Matching Using A Mesh Rings and Mesh Points The ring wear has little effect on the feature matching, if it is limited in size and number

25 Feature Matching Using A Mesh “finger-brought-together” image instead of the pegs “stretched-or-straightened” image instead of the flat bottom plate the bending is not so fatal, if it is urged to stretch or straighten out

26 Results  Database ：  50 users  Each user’s hand ：  10 images were captured (total of 500 images ).

27 Feature Matching Using A Mesh Genuine and imposter rmsd distribution

28 Conclusion Our matching is multistaged :  the first stage is matching for the authentication  the second stage is based on four-finger procedure as a usual matching  the third stage is based on more detailed geometric parameters such as the shape factors of each finger section or the palm

29 Conclusion This point-based matching brings about a robust and real-time processing of less than one second The “brought-together fingers” and “stretched-and-straightened- out palm” are our instructions to the user this noncontacting personal feature extraction method will easily in combination with the hand geometry, palm vascular pattern, and/or facial processing

30 References A. K. Jain and A. Ross, “A prototype hand geometry-based verification system,” in Proc. 2nd Int. Conf. Audio- and Video-based Biometric Personal Authentication (AVBPA), 1999, pp. 166–171 N. Duta, A. K. Jain, and K. Mardia, “Matching of palmprint,” Pattern Recognit. Lett., vol. 23, pp. 477–485, 2002 R. Sanchez-Reillo, C. Sanchez-Avila, and A. Gonzalez-Marcos, “Biometric identification threou hand geometry measurements,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1168–1171, Oct. 2000