1. Marco Maisto, Massimo Panella, Luca Liparulo, and Andrea Proietti
An Accurate Algorithm for the Identification of Fingertips Using an RGB-D Camera
Marco Maisto, Massimo Panella, Luca Liparulo, and Andrea Proietti
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, VOL. 3, NO. 2, JUNE 2013
Speaker：Yi-Ting Chen

2. Outline

3. Introduction

4. New Approach to Finger Recognition

5. Sampling and Segmentation
Use the “hand-point” control returned by NiTE.
Segmentation of the hand image detected by the Kinect sensor.
Build a matrix with depth information
check the distance from the hand-point for every pixels, if it is less than a threshold, the point will be identified as belonging to the hand

6. Noise Canceling Use functions from OpenCV library[45].
cvErode can be seen as the computation of local minima on the kernel area.
cvMorphologyEx will compute the geometric shape of the hand

7. Hand Contour Detection
Use the cvFindContours function from OpenCV to find the contour of the hand.
Return a pointer to a sequence containing all the points regarding the hand contour.

8. Contour Approximation
using the cvApproxPoly function from OpenCV
approximates one or more curves using the well-known “Douglas-Peucker” algorithm

9. Computation of the Center of Mass
The function cvMoment in OpenCV returns image moments up to the third order, allowing the straightforward determination of the CoM.

10. Determination of the Convex Envelope
The cvConvexHull function of OpenCV is used to retrieve the envelope of an object.
This function uses the Sklansky’s algorithm [47] to find the convex envelope of an n-dimensional polygon.

11. Computation of the Convexity Defects
using the OpenCV function cvConvexityDefects

12. Identification of fingertips
Three main variations of Fingertip Tracking algorithm
convexity-defects-based (CDB)
center-of-mass-based (CMB)
geometry-based variation (GB)

13. FT-CDB Method
Incorrect when the hand is pointing down.
𝑌 𝑃 𝑖
𝑌 𝐶

14. FT-CMB Method
Counting whether the top points are more than the bottom ones to determine hand is up or down.

15. FT-CMB Method
Might have a problem when the hand is partially closed.

16. FT-GB Method

17. Illustrative Tests

18. Identification Rate

19. Execution Speed
The average values obtained over a launch of iterations of the algorithm are 1.5, 2.5, and 3.5 ms for FT-CDB, FT-CMB, and FT-GB
Kinect operates at a target rate of 30 fps and hence, the frame period is about 33ms.

20. Precision of the Identification

21. Accuracy of the Identification

22. Comparative Analysis With Existing Techniques