Hand Gesture Controls for PPT Presentations
K-NN Classification is used to recognise various hand gestures and control PowerPoint presentations.
The interface between people and computers nowadays heavily relies on gesture recognition. To make it easier for people and computers to communicate simply and easily, It is possible to interface with machines using gestures rather than tools like computers, laser pens, etc. Users of the proposed system may operate the SLIDESHOW Presentation using four straightforward gestures without ever touching the PC.
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The slide-show display is exclusively controlled by static hand motions in the suggested method. These motionless hand movements are segmented before being analysed further. The image may be used to extract Histogram-of-gradient feature after segmentation and processing. After HOG feature extraction, the extracted features are compared using k-nearest neighbour classification to the characteristics of the gesture picture recorded in the database. The slideshow is managed and the relevant action in relation to that specific gesture is executed.
|Go to I Slide|
|End Of Presentation|
TABLE I. shows the four gestures that have been made by the user in the proposed system. They are as follows:
- One finger.
- All fingers folded.
- Four fingers.
- Gesture good.
This system uses a straightforward USB 2 PC WEB-CAMERA to record input images of motions. A new picture resolution is then obtained by resizing the acquired image. The picture's elements are segmented after which they are transformed into an unsigned 8-bit image. After that, thresholding and background subtraction are completed. The histogram of features extraction method is used to extract the characteristics from the given image.
Using K-Nearest Neighbour classification, the input image's features are now compared to the features of a database of 100 photos for each gesture.
The computer labels the test image as one of the four gestures in the form by comparing its properties to those of the database of four gestures.
The distance metric which is mostly used in K-NN Classification is the Euclidean Distance.
The formula for calculating the Euclidean distance between the two images is given below.
Where P is the input image's feature vector, Q is the database image's feature vector, and D stands for Euclidean Distance.
The suggested system has been implemented using D=1, D=3, and D=5 as the three distinct Euclidean distances. For various levels of D, the system's overall accuracy varies. The system's overall accuracy is 85% when D = 1, 78% when D = 3, and 81% when D = 5.
The slideshow is then managed and the required action in relation to that specific gesture is taken.
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