A Vision-Based Method to Find Fingertips in a Closed Hand

Title

A Vision-Based Method to Find Fingertips in a Closed Hand

Ankit Chaudhary*, Kapil Vatwani*, Tushar Agrawal* and J.L. Raheja**

Introduction

The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. The proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges.

Methodology

The presented method for fingertips detection was divided into three

• The first section performed the pre-processing of the image obtained from the web camera to separate the ROI. 
• The second section involved the application of various edge detection algorithms to the pre-processed image to separate the edges of the hand and the fingers.
• The final section discusses the application of the corner detection algorithms to the various edge filtered images to obtain the corners in these images.Finally, the interested corner points of fingertips were extracted and uninterested corner points were rejected using proper conditions.

 1 Pre-Processing

First, the area of interest was separated from the background using color differences from the background environment and background subtraction. In this case, the background environment was taken as a white wall for the initial results. The conditions for filtering the hand from background were as follows:

• 􀂃 Pixel’s Red color should be 10% greater than its green color.
• 􀂃 Pixel’s Red color should be 5% greater than its blue color.
• 􀂃 Pixel should not have a green color and a blue color over a 60% of the maximum.

Then applied the thresholding and get the binary image of the hand.

2 Edge Detection

Three different filters were tried on the input images to detect the edges. These three methods were the Canny filter, Laplace filter and Gabor filter.

3 Corner Detection and Fingertip Localization

·         Corner detection involved the detection of corners with big Eigen values.

•  The program first calculated the minimal Eigen value for every source image pixel and stored them in another image.
•  It then performed non-maxima suppression where only local maxima in 3x3 neighborhoods remained.
•  The next step was to reject the corners with the minimal Eigen value less than quality level, with a user-specified quality value.
• Finally, the function ensured that all the corners found were distanced enough from one another.This was done by considering the corners and checking that the distance between the newly considered feature and the features considered earlier was larger than min distance (user-defined). The function therefore removed the features that were too close to the stronger features.

Results and conclusion

The accuracy of the corner detection algorithm was tested by applying it to various images such as the RGB image, Canny edge filtered image, Laplace edge filtered image and Gabor filtered image. Out of these filters, the Gabor filter resulted in satisfactory results.

The system was tested in different conditions for a long time to check its robustness with different users. The system was free from the user’s hand geometry and would work the same for everyone and results showed 50-60% accuracy.

The things that abstract from this research paper to our project

 According to our project we have to propose the algorithm to identify the signs for open hand as well as closed hand. We can develop above algorithm to identify the signs of closed hand.