Published on Sep 28, 2024
With the ever advancing technology, there is a growing need for systems that provide better human interaction. Human gestures are one such way in which users can interact with the system in a user-friendly, cost-effective and time efficient manner. This gesture recognition is used in a wide variety of applications like Gaming, Object/Motion Tracking, and System Application Control etc.
Our project demonstrates a few applications of gesture recognition, with main emphasis on application control using gestures. Some of these applications include: Mouse Control using Gestures .All these applications are designed to use the integrated webcam of any computer. The webcam is used to capture live video frames which are then processed to identify corresponding gestures for corresponding applications.
We use Head gestures as the inputs for gestures. The recognized gestures are then mapped to corresponding actions. The accuracy of the system can also be improved by using webcams with better range and resolutions.
Gestures Recognition System Using Privacy aims to demonstrate the use of gestures for control of various system applications by means of Head movement with a level of privacy. Our project provides a way in which users can interact with the system easily in a user-friendly manner to control mouse and thereby control a wide variety of other applications running on the system by means of mouse operations.
It also allows controlling a game application using gestures, which is in turn designed to be controlled using mouse movement. Thus, even applications that are not designed to work with gestures directly can be made to be controlled using Head gestures. In our system, the user needs only to move his/her Head to perform all these operations.
Our project demonstrates a few applications of gesture recognition, with main emphasis on application control using Head gestures and Privacy .Every user should have Privacy whilst on their work. Users often surf their laptops on public places and they might access their credit card numbers, bank account number, or may fill their username and password etc. in such a case the user fall to the victim of shoulder surfing.
In computer security, shoulder surfing refers to using direct observation techniques, such as looking over someone's shoulder, to get information. Shoulder surfing is an effective way to get information in crowded places because it's relatively easy to stand next to someone and watch as they fill out a form, enter a PIN number at an ATM machine, or use a calling card at a public pay phone. Shoulder surfing can also be done long distance with the aid of binoculars or other vision-enhancing devices. So the user who has worn the customizable glass will only be able to view the activity on the monitor, other people who have not work the customizable glass won’t be able to see the desktop .Thus by integration of the customizable glass and monitor with the gesture based recognition the user can view the desktop environment and navigate in it, without being a Victim to Shoulder Surfing.
Every user should have Privacy whilst on their work. Users often surf their laptops on public places and they might access their credit card numbers ,bank account number, or may fill their username and password etc….in such case the user fall to the victim of shoulder surfing. Shoulder surfing: In computer security, shoulder surfing refers to using direct observation techniques, such as looking over someone's shoulder, to get information.Thereby Only the user should be able to see the contents on his screen, others must not see the contents of the screen. Whenever the light passes from the first polarizer sheet, the screen floods with garbage light so when the light passes another polarizer then the garbage light gets filtered and the screen. Row of LED lights at the back of the Monitor. These are the only lights in the monitor. Now the optical System mask the light even across the monitor.
1. The first sheet makes a nice even background for the light.
2. The next piece is called the light guide plate. It is covered with dots, when light enters from the bottom edge it propagate down the plates hitting some of the dots , thereby having a total internal reflection and makes the light rays emerge from the front.
3. Then a diffuser film is place that eliminated the dark pattern from the light guide plate.
4. Then a prism film is placed .The backlight emerges from the light guide plate at many angles. This sheet increases the perpendicular component.
5. Then finally last diffuser film is put on to have a evenly distributed light all coming from the row of single led lights from the bottom.
At the back and front of the sheets are the two polarizers, they stick tightly with the piece of the glass. The bottom polarizer will create polarized light which will only pass through another polarizer with the same Orientation. When the polarizers are oriented perpendicular to each other so that light could pass from each other that mode is called as normally white mode.
1. Light polarization not changes so it cannot pass through front polarizer.
2. The colors are changed by alternating the intensity of the light.
3. The polarized sheet is sheared from the monitor screen and is cut out.
There are two antiglare films one in the front side and the other in the back side of the polarizer film. So we have to remove both the antiglare film. Antiglare film limit the focal length to less than a cm, so in order to make the focal length infinite, the antiglare film is removed. The payoff is, some glaring can be seen in the screen.
In this step we configure the setting for the OpenCV environment by making the OpenCV build using Cmake. Cmake is used for making the binary files for OpenCV so that it can be worked upon visual studio. We write an OpenCV application and use various OpenCV algorithms on images such as histogram, RBG levels, Grayscale. etc. Connect the OpenCV application with the web cam and receive video input through the webcam in OpenCV application. The webcam port is connected to the hardware port through Visual studio. The webcam is external one, so we have to connect the webcam to the address of the external not internal.
In this phase of the methodology, we will program an application that will track the Head gesture of user. We will use various OpenCV algorithms to trace the color of the user. We will maintain a pixel counter and a threshold for the pixel boundary, so that if the boundary is exceeded we can trigger an action. The action involves tracking the color of skin where ever it moves in the viewing area of the window via Camshift algorithm. Camshift is an open-source algorithm of OpenCV libraries which tracks color via binary thresholding.
We have programmed an application in VC++ that will link the OpenCV application to the mouse pointer, so that the change in the value of the image pixels will result in the movement of mouse, it can be used to play a simple window’s game. We have integrated both the OpenCV application as well as the Privacy monitor to enhance the entire Architecture of the System so that it will be a Head gesture Recognition system with Privacy to user’s working Environment.
This project Application Control using Gestures is developed to assist a wide variety of users in controlling system applications by means of simple Head gestures. This project is very useful in a number of real-time scenarios. For instance, a person giving a PowerPoint demonstration can use this system to browse through the slides. This is also useful for users with motor neuron disabilities who cannot perform click operations. This Project can help to eliminate Shoulder surfing which is a security threat. This idea can be implemented in banks, ATM’s etc… to add a level of security. This can also be implemented in Laptops, LCD Displays, Smart phones...etc. .
This Project can also help eliminate piracy of movies as in theatres. This system can be incorporated at high level security chambers where data’s are highly confidential. It has a Great marketing potential in the field of games as well. The software used here are open source so, a lot of research can be done in this area. The hardware costs are also cheap, so from the marketing point of view it is also beneficial.
Despite its many uses, Application Control using Gestures does have its own limitations. The monitor glare can be reduced by using different kind of polarizer and by using different materials. All the while, it has a lot of future scope. The system can be improved in terms of complexity of recognized gestures, accuracy, and performance and can also be extended to assist in complicated real time applications like sign to speech conversion for dumb people etc…
The hardware and software requirements for the development phase of our project are:
�� Operating System Requirements : Windows XP/Vista/Linux.
�� Packages: OpenCV 2.3, Microsoft visual studio 2010, .Net Framework 4.0 , Cmake 2.8.
�� Languages Used : Visual c++, OpenCv Libraries.
�� Drivers: Web Cam Drivers.
�� Customised LCD monitor.
�� Customized wearable Spectacles.
�� Pentium processor (preferable).
�� Hard disk: 40 GB (minimum).
�� Graphics card support needed. NVIDIA 8600GS Used.
�� Ram: 256 MB (minimum).