Nabeel Fasal/PET Engineering College
For a long time research on human-computer interaction (HCI) has been restricted to techniques based on the use of monitor, keyboard and mouse. Recently this paradigm has changed. Techniques such as vision, sound, speech recognition, projective displays and location aware devices allow for a much richer, multi-modal interaction between man and machine.
Finger-tracking is usage of bare hand to operate a computer in order to make human-computer interaction much more faster and easier.
Fingertip finding deals with extraction of information from hand features and positions. In this method we use the position and direction of the fingers in order to get the required segmented region of interest.
Finger pointing systems aim to replace pointing and clicking devices like the mouse with the bare hand. These applications require a robust localization of the fingertip plus the recognition of a limited number of hand postures for “clicking-commands”. Finger-tracking systems are considered as specialized type of hand posture/gesture recognition system.
The typical Specializations are:
1) Only the most simple hand postures and recognized. 2) The hand usually covers a part of the on screen. 3) The finger positions are being found in real-time
4) Ideally, the system works with all kinds of backgrounds 5) The system does not restrict the speed of hand movements
In finger –tracking systems except that the real-time constraints currently do not allow sophisticated approaches such as 3D-model matching or Gabor wavelets.
1. Color Tracking Systems:
Queck build a system called “FingerMouse”, which allows control of the mouse pointer with the fingertip ([Queck 95]). To perform a mouse-click the user has to press the shift key on the keyboard. Queck argues that 42% of the mouse-selection-time is actually used to move the hand from the keyboard to the mouse and back. Most of this time can be saved with the FingerMouse system. The tracking works at about 15Hz and uses color look-up tables to segment the finger (see Figure 1). The pointing posture and the fingertip position are found by applying some simple heuristics on the line sums of the segmented image.
Figure 1: (a) The FingerMouse setup (b) Color segmentation result
2.Correlation Tracking Systems
Correlation yields good tracking results, as
long as the background is relatively uniform and the tracked object moves slowly. Correlation works performs well with slow movements; but it can only search a small part of the image and therefore fails if the finger is moving too fast. Crowley and Bérard used correlation tracking to build a system called “FingerPaint,” which allows the user to “paint” on the wall with the bare finger ([Crowley 95]). The system tracks the finger position in real-time and redisplays it with a projector to the wall (see Figure 2.a). Moving the finger into a trigger region initializes the correlation.Mouse down detection was simulated using the space bar of the keyboard.
Figure 2: (a) FingerPaint system (from [Crowley 95]) (b) The Digital Desk (from [Well 93]) (c) Television control with the hand (from [Freeman 95])
FingerPaint was inspired by the “digital desk” described in [Well 93], which also uses a combination of projector and camera to create an augmented reality (see Figure 2.b). Well’s system used image differencing to find the finger. The big drawback is that it does not work well if the finger is not moving. Freeman used correlation to track the whole hand and to discriminate simple gestures. He applied the system to build a...